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MON 25 APR
midnight
Remarks:
Opening Remarks
(ends 12:55 AM)
1 a.m.
Invited Talk:
Leveraging AI for Science
Pushmeet Kohli
(ends 2:15 AM)
2:30 a.m.
Poster Session 1 [2:30-4:30]
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning
Constrained Policy Optimization via Bayesian World Models
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Evidential Turing Processes
Generalisation in Lifelong Reinforcement Learning through Logical Composition
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
Learning Temporally Causal Latent Processes from General Temporal Data
Bootstrapping Semantic Segmentation with Regional Contrast
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Training Structured Neural Networks Through Manifold Identification and Variance Reduction
Scale Mixtures of Neural Network Gaussian Processes
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
Sample and Computation Redistribution for Efficient Face Detection
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs
Bayesian Neural Network Priors Revisited
Perceiver IO: A General Architecture for Structured Inputs & Outputs
Topological Graph Neural Networks
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
Progressive Distillation for Fast Sampling of Diffusion Models
Signing the Supermask: Keep, Hide, Invert
Generative Modeling with Optimal Transport Maps
Autoregressive Diffusion Models
Imbedding Deep Neural Networks
Top-label calibration and multiclass-to-binary reductions
High Probability Generalization Bounds with Fast Rates for Minimax Problems
Steerable Partial Differential Operators for Equivariant Neural Networks
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing
The Three Stages of Learning Dynamics in High-dimensional Kernel Methods
The Role of Pretrained Representations for the OOD Generalization of RL Agents
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
End-to-End Learning of Probabilistic Hierarchies on Graphs
Spanning Tree-based Graph Generation for Molecules
8-bit Optimizers via Block-wise Quantization
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
On the Existence of Universal Lottery Tickets
On Incorporating Inductive Biases into VAEs
Controlling the Complexity and Lipschitz Constant improves Polynomial Nets
Stability Regularization for Discrete Representation Learning
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
Boosted Curriculum Reinforcement Learning
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Boosting the Certified Robustness of L-infinity Distance Nets
Efficient Neural Causal Discovery without Acyclicity Constraints
Unsupervised Disentanglement with Tensor Product Representations on the Torus
Learning to Generalize across Domains on Single Test Samples
Hierarchical Variational Memory for Few-shot Learning Across Domains
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Goal-Directed Planning via Hindsight Experience Replay
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder
Variational Neural Cellular Automata
Random matrices in service of ML footprint: ternary random features with no performance loss
Adversarial Retriever-Ranker for Dense Text Retrieval
Joint Shapley values: a measure of joint feature importance
Non-Parallel Text Style Transfer with Self-Parallel Supervision
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search
A Fine-Grained Analysis on Distribution Shift
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Memory Replay with Data Compression for Continual Learning
Anytime Dense Prediction with Confidence Adaptivity
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms
(ends 4:30 AM)
4 a.m.
9 a.m.
Invited Talk:
Beyond interpretability: developing a language to shape our relationships with AI
Been Kim
(ends 10:15 AM)
10:30 a.m.
Poster Session 2 [10:30-12:30]
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Learning Transferable Reward for Query Object Localization with Policy Adaptation
The Spectral Bias of Polynomial Neural Networks
A Generalized Weighted Optimization Method for Computational Learning and Inversion
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
Critical Points in Quantum Generative Models
Gradient Importance Learning for Incomplete Observations
Learning to Guide and to be Guided in the Architect-Builder Problem
Understanding Intrinsic Robustness Using Label Uncertainty
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Efficient Self-supervised Vision Transformers for Representation Learning
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
PAC Prediction Sets Under Covariate Shift
Fast Model Editing at Scale
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning
On Predicting Generalization using GANs
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Illiterate DALL-E Learns to Compose
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Salient ImageNet: How to discover spurious features in Deep Learning?
Distributional Reinforcement Learning with Monotonic Splines
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Environment Predictive Coding for Visual Navigation
Revisiting Design Choices in Offline Model Based Reinforcement Learning
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model
Vision-Based Manipulators Need to Also See from Their Hands
Chunked Autoregressive GAN for Conditional Waveform Synthesis
An Experimental Design Perspective on Model-Based Reinforcement Learning
Quadtree Attention for Vision Transformers
Optimizer Amalgamation
Information-theoretic Online Memory Selection for Continual Learning
How Much Can CLIP Benefit Vision-and-Language Tasks?
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Scalable Sampling for Nonsymmetric Determinantal Point Processes
Coordination Among Neural Modules Through a Shared Global Workspace
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Fast AdvProp
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning
Real-Time Neural Voice Camouflage
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Hierarchical Few-Shot Imitation with Skill Transition Models
Spherical Message Passing for 3D Molecular Graphs
Anisotropic Random Feature Regression in High Dimensions
Language-driven Semantic Segmentation
Fast topological clustering with Wasserstein distance
Stochastic Training is Not Necessary for Generalization
Modular Lifelong Reinforcement Learning via Neural Composition
Exploring the Limits of Large Scale Pre-training
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Offline Reinforcement Learning with Implicit Q-Learning
Continual Learning with Filter Atom Swapping
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
FairCal: Fairness Calibration for Face Verification
Should I Run Offline Reinforcement Learning or Behavioral Cloning?
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
Privacy Implications of Shuffling
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
Graph Condensation for Graph Neural Networks
Auto-Transfer: Learning to Route Transferable Representations
Adversarial Support Alignment
Incremental False Negative Detection for Contrastive Learning
Hyperparameter Tuning with Renyi Differential Privacy
Effect of scale on catastrophic forgetting in neural networks
EigenGame Unloaded: When playing games is better than optimizing
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Noisy Feature Mixup
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
DEGREE: Decomposition Based Explanation for Graph Neural Networks
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
Efficiently Modeling Long Sequences with Structured State Spaces
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
Learning by Directional Gradient Descent
Relating transformers to models and neural representations of the hippocampal formation
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
Network Augmentation for Tiny Deep Learning
DictFormer: Tiny Transformer with Shared Dictionary
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.
Deep AutoAugment
Chemical-Reaction-Aware Molecule Representation Learning
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
Igeood: An Information Geometry Approach to Out-of-Distribution Detection
Visual Correspondence Hallucination
Continuous-Time Meta-Learning with Forward Mode Differentiation
How to deal with missing data in supervised deep learning?
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption
Fair Normalizing Flows
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
Contextualized Scene Imagination for Generative Commonsense Reasoning
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks
Controlling Directions Orthogonal to a Classifier
Neural Relational Inference with Node-Specific Information
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
Mapping Language Models to Grounded Conceptual Spaces
RegionViT: Regional-to-Local Attention for Vision Transformers
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Actor-critic is implicitly biased towards high entropy optimal policies
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
(ends 12:30 PM)
noon
Affinity Social:
LatinX in AI (LXAI) Social @ ICLR 2022
(ends 4:59 PM)
Affinity Social:
WiML Virtual Social
(ends 2:00 PM)
5 p.m.
Oral 1: AI Applications [5:00-6:30]
Oral s 5:00-6:30
[5:00] Language modeling via stochastic processes
[5:15] MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
[5:30] Real-Time Neural Voice Camouflage
[5:45] ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
[6:00] Open-Set Recognition: A Good Closed-Set Classifier is All You Need
[6:15] Vision-Based Manipulators Need to Also See from Their Hands
(ends 6:30 PM)
Oral 1: Learning in the wild, Reinforcement learning [5:00-6:30]
Oral s 5:00-6:30
[5:00] Hyperparameter Tuning with Renyi Differential Privacy
[5:15] PiCO: Contrastive Label Disambiguation for Partial Label Learning
[5:30] Poisoning and Backdooring Contrastive Learning
[5:45] Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
[6:00] The Information Geometry of Unsupervised Reinforcement Learning
[6:15] Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
(ends 6:30 PM)
6:30 p.m.
Poster Session 3 [6:30-8:30]
Neural Processes with Stochastic Attention: Paying more attention to the context dataset
Geometric Transformers for Protein Interface Contact Prediction
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
MoReL: Multi-omics Relational Learning
Generalized Decision Transformer for Offline Hindsight Information Matching
Equivariant Transformers for Neural Network based Molecular Potentials
Path Integral Sampler: A Stochastic Control Approach For Sampling
GNN is a Counter? Revisiting GNN for Question Answering
BiBERT: Accurate Fully Binarized BERT
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Task Relatedness-Based Generalization Bounds for Meta Learning
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Generative Models as a Data Source for Multiview Representation Learning
Focus on the Common Good: Group Distributional Robustness Follows
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Emergent Communication at Scale
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning
Towards Continual Knowledge Learning of Language Models
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
FastSHAP: Real-Time Shapley Value Estimation
An Unconstrained Layer-Peeled Perspective on Neural Collapse
Efficient and Differentiable Conformal Prediction with General Function Classes
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder
Rethinking Adversarial Transferability from a Data Distribution Perspective
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
Fast Generic Interaction Detection for Model Interpretability and Compression
Compositional Training for End-to-End Deep AUC Maximization
Representational Continuity for Unsupervised Continual Learning
What Do We Mean by Generalization in Federated Learning?
Self-Supervision Enhanced Feature Selection with Correlated Gates
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
GreaseLM: Graph REASoning Enhanced Language Models
Objects in Semantic Topology
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
Understanding the Role of Self Attention for Efficient Speech Recognition
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Online Coreset Selection for Rehearsal-based Continual Learning
On Evaluation Metrics for Graph Generative Models
ViTGAN: Training GANs with Vision Transformers
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Neural Models for Output-Space Invariance in Combinatorial Problems
Variational oracle guiding for reinforcement learning
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Automated Self-Supervised Learning for Graphs
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
Target-Side Input Augmentation for Sequence to Sequence Generation
Language modeling via stochastic processes
Neural Program Synthesis with Query
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval
On the Learning and Learnability of Quasimetrics
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Weighted Training for Cross-Task Learning
Probabilistic Implicit Scene Completion
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Trigger Hunting with a Topological Prior for Trojan Detection
Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
Continual Learning with Recursive Gradient Optimization
The Close Relationship Between Contrastive Learning and Meta-Learning
FedBABU: Toward Enhanced Representation for Federated Image Classification
Conditional Image Generation by Conditioning Variational Auto-Encoders
Training invariances and the low-rank phenomenon: beyond linear networks
On Robust Prefix-Tuning for Text Classification
Regularized Autoencoders for Isometric Representation Learning
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting
Decoupled Adaptation for Cross-Domain Object Detection
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum
Taming Sparsely Activated Transformer with Stochastic Experts
On Non-Random Missing Labels in Semi-Supervised Learning
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Data-Efficient Graph Grammar Learning for Molecular Generation
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Unsupervised Discovery of Object Radiance Fields
(ends 8:30 PM)
8 p.m.
Affinity Social:
Queer in AI Social @ ICLR 2022
(duration 1.0 hr)
TUE 26 APR
1 a.m.
Oral 2: Structured learning [1:00-2:30]
Oral s 1:00-2:30
[1:00] Understanding over-squashing and bottlenecks on graphs via curvature
[1:15] Efficiently Modeling Long Sequences with Structured State Spaces
[1:30] Neural Structured Prediction for Inductive Node Classification
[1:45] A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
[2:00] CycleMLP: A MLP-like Architecture for Dense Prediction
[2:15] Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
(ends 2:30 AM)
Oral 2: Understanding Deep Learning [1:00-2:45]
Oral s 1:00-2:45
[1:00] Expressiveness and Approximation Properties of Graph Neural Networks
[1:15] Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
[1:30] Learning Strides in Convolutional Neural Networks
[1:45] The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions
[2:00] Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
[2:15] DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS
[2:30] Representational Continuity for Unsupervised Continual Learning
(ends 2:45 AM)
Oral 2: AI applications [1:00-2:30]
Oral s 1:00-2:30
[1:00] Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
[1:15] Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
[1:30] Data-Efficient Graph Grammar Learning for Molecular Generation
[1:45] iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
[2:00] Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation
[2:15] StyleAlign: Analysis and Applications of Aligned StyleGAN Models
(ends 2:30 AM)
2:30 a.m.
Poster Session 4 [2:30-4:30]
Divergence-aware Federated Self-Supervised Learning
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Self-ensemble Adversarial Training for Improved Robustness
L0-Sparse Canonical Correlation Analysis
Learning meta-features for AutoML
Learning Optimal Conformal Classifiers
PiCO: Contrastive Label Disambiguation for Partial Label Learning
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models
Large-Scale Representation Learning on Graphs via Bootstrapping
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator
Neural Variational Dropout Processes
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Sound Adversarial Audio-Visual Navigation
Zero Pixel Directional Boundary by Vector Transform
NeuPL: Neural Population Learning
A generalization of the randomized singular value decomposition
Explainable GNN-Based Models over Knowledge Graphs
Handling Distribution Shifts on Graphs: An Invariance Perspective
Self-Supervised Inference in State-Space Models
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Transformers Can Do Bayesian Inference
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Do We Need Anisotropic Graph Neural Networks?
Fooling Explanations in Text Classifiers
Learning Strides in Convolutional Neural Networks
Distributionally Robust Models with Parametric Likelihood Ratios
Unrolling PALM for Sparse Semi-Blind Source Separation
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Generative Principal Component Analysis
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Enabling Arbitrary Translation Objectives with Adaptive Tree Search
Resolving Training Biases via Influence-based Data Relabeling
Visual Representation Learning over Latent Domains
Revisiting Over-smoothing in BERT from the Perspective of Graph
Label-Efficient Semantic Segmentation with Diffusion Models
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
VAE Approximation Error: ELBO and Exponential Families
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Augmented Sliced Wasserstein Distances
Rethinking Supervised Pre-Training for Better Downstream Transferring
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
Delaunay Component Analysis for Evaluation of Data Representations
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
Relational Learning with Variational Bayes
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
On the approximation properties of recurrent encoder-decoder architectures
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Amortized Implicit Differentiation for Stochastic Bilevel Optimization
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix
OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION
Leveraging Automated Unit Tests for Unsupervised Code Translation
Top-N: Equivariant Set and Graph Generation without Exchangeability
A First-Occupancy Representation for Reinforcement Learning
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
A Program to Build E(N)-Equivariant Steerable CNNs
MAML is a Noisy Contrastive Learner in Classification
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"
Phase Collapse in Neural Networks
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization
Constrained Physical-Statistics Models for Dynamical System Identification and Prediction
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
Imitation Learning by Reinforcement Learning
Learning to Downsample for Segmentation of Ultra-High Resolution Images
The Evolution of Uncertainty of Learning in Games
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling
Gradient Step Denoiser for convergent Plug-and-Play
(ends 4:30 AM)
9 a.m.
Invited Talk:
Representation Learning in the Global South: Societal Considerations-Fairness, Safety and Privacy
John Amuasi
(ends 10:15 AM)
10:30 a.m.
Poster Session 5 [10:30-12:30]
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
Know Your Action Set: Learning Action Relations for Reinforcement Learning
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
Label Encoding for Regression Networks
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
Optimizing Neural Networks with Gradient Lexicase Selection
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Interpretable Unsupervised Diversity Denoising and Artefact Removal
Tighter Sparse Approximation Bounds for ReLU Neural Networks
Policy Gradients Incorporating the Future
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
VC dimension of partially quantized neural networks in the overparametrized regime
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
Generalized Kernel Thinning
Policy Smoothing for Provably Robust Reinforcement Learning
Transfer RL across Observation Feature Spaces via Model-Based Regularization
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
Variational methods for simulation-based inference
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
Towards Model Agnostic Federated Learning Using Knowledge Distillation
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Learning Value Functions from Undirected State-only Experience
Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Fairness Guarantees under Demographic Shift
Multitask Prompted Training Enables Zero-Shot Task Generalization
Learning Neural Contextual Bandits through Perturbed Rewards
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
Data-Driven Offline Optimization for Architecting Hardware Accelerators
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
Differentiable Scaffolding Tree for Molecule Optimization
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer
Maximizing Ensemble Diversity in Deep Reinforcement Learning
One After Another: Learning Incremental Skills for a Changing World
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
Revisiting flow generative models for Out-of-distribution detection
GradMax: Growing Neural Networks using Gradient Information
A global convergence theory for deep ReLU implicit networks via over-parameterization
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Improving Non-Autoregressive Translation Models Without Distillation
Finetuned Language Models are Zero-Shot Learners
Efficient Active Search for Combinatorial Optimization Problems
Subspace Regularizers for Few-Shot Class Incremental Learning
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
Planning in Stochastic Environments with a Learned Model
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ENTROPY CONSTRAINED OPTIMAL TRANSPORT
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
Leveraging unlabeled data to predict out-of-distribution performance
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
GradSign: Model Performance Inference with Theoretical Insights
The MultiBERTs: BERT Reproductions for Robustness Analysis
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Provable Adaptation across Multiway Domains via Representation Learning
Open-Set Recognition: A Good Closed-Set Classifier is All You Need
Discrete Representations Strengthen Vision Transformer Robustness
Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
Permutation-Based SGD: Is Random Optimal?
A Fine-Tuning Approach to Belief State Modeling
Curriculum learning as a tool to uncover learning principles in the brain
Scaling Laws for Neural Machine Translation
Is Importance Weighting Incompatible with Interpolating Classifiers?
Prototype memory and attention mechanisms for few shot image generation
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
The Uncanny Similarity of Recurrence and Depth
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
A NON-PARAMETRIC REGRESSION VIEWPOINT : GENERALIZATION OF OVERPARAMETRIZED DEEP RELU NETWORK UNDER NOISY OBSERVATIONS
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
An Explanation of In-context Learning as Implicit Bayesian Inference
Synchromesh: Reliable Code Generation from Pre-trained Language Models
Online Adversarial Attacks
On the Uncomputability of Partition Functions in Energy-Based Sequence Models
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
On the Role of Neural Collapse in Transfer Learning
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings
MetaMorph: Learning Universal Controllers with Transformers
Stiffness-aware neural network for learning Hamiltonian systems
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Bootstrapped Meta-Learning
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
BAM: Bayes with Adaptive Memory
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps
Feature Kernel Distillation
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease
Meta-Learning with Fewer Tasks through Task Interpolation
Predicting Physics in Mesh-reduced Space with Temporal Attention
Provably Robust Adversarial Examples
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method
Granger causal inference on DAGs identifies genomic loci regulating transcription
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations
Expressiveness and Approximation Properties of Graph Neural Networks
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
Evaluating Distributional Distortion in Neural Language Modeling
Triangle and Four Cycle Counting with Predictions in Graph Streams
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
Dual Lottery Ticket Hypothesis
Selective Ensembles for Consistent Predictions
(ends 12:30 PM)
noon
Affinity Social:
Queer in AI Social @ ICLR 2022
(duration 1.0 hr)
1 p.m.
3 p.m.
5 p.m.
Invited Talk:
‘Affordances’ for Machine Learning
Jenny Davis
(ends 6:15 PM)
6:30 p.m.
Poster Session 6 [6:30-8:30]
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
TAPEX: Table Pre-training via Learning a Neural SQL Executor
Causal Contextual Bandits with Targeted Interventions
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Multi-Mode Deep Matrix and Tensor Factorization
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Sampling with Mirrored Stein Operators
Towards Understanding the Data Dependency of Mixup-style Training
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
Learning Towards The Largest Margins
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Transformer Embeddings of Irregularly Spaced Events and Their Participants
Conditional Contrastive Learning with Kernel
Training Transition Policies via Distribution Matching for Complex Tasks
Procedural generalization by planning with self-supervised world models
Backdoor Defense via Decoupling the Training Process
Multi-objective Optimization by Learning Space Partition
Skill-based Meta-Reinforcement Learning
Differentially Private Fine-tuning of Language Models
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
TAda! Temporally-Adaptive Convolutions for Video Understanding
Learning to Complete Code with Sketches
Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning
Equivariant Graph Mechanics Networks with Constraints
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch
Maximum n-times Coverage for Vaccine Design
Recursive Disentanglement Network
Bandit Learning with Joint Effect of Incentivized Sampling, Delayed Sampling Feedback, and Self-Reinforcing User Preferences
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Pareto Policy Pool for Model-based Offline Reinforcement Learning
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Open-World Semi-Supervised Learning
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
Neural Structured Prediction for Inductive Node Classification
Dynamics-Aware Comparison of Learned Reward Functions
Learning Features with Parameter-Free Layers
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
On feature learning in neural networks with global convergence guarantees
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Learning to Annotate Part Segmentation with Gradient Matching
Strength of Minibatch Noise in SGD
Optimal Representations for Covariate Shift
Discovering Invariant Rationales for Graph Neural Networks
Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining
Knowledge Removal in Sampling-based Bayesian Inference
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
Fixed Neural Network Steganography: Train the images, not the network
Inductive Relation Prediction Using Analogy Subgraph Embeddings
Accelerated Policy Learning with Parallel Differentiable Simulation
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Sparse Attention with Learning to Hash
Is Homophily a Necessity for Graph Neural Networks?
DKM: Differentiable k-Means Clustering Layer for Neural Network Compression
Creating Training Sets via Weak Indirect Supervision
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
Neural Deep Equilibrium Solvers
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Towards General Function Approximation in Zero-Sum Markov Games
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Disentanglement Analysis with Partial Information Decomposition
Bi-linear Value Networks for Multi-goal Reinforcement Learning
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Neural Stochastic Dual Dynamic Programming
(ends 8:30 PM)
WED 27 APR
1 a.m.
Invited Talk:
Do you see what I see? Large-scale learning from multimodal videos
Cordelia Schmid
(ends 2:15 AM)
2:30 a.m.
Poster Session 7 [2:30-4:30]
Deep Attentive Variational Inference
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
Mapping conditional distributions for domain adaptation under generalized target shift
Normalization of Language Embeddings for Cross-Lingual Alignment
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
On the Optimal Memorization Power of ReLU Neural Networks
On Redundancy and Diversity in Cell-based Neural Architecture Search
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
Model Agnostic Interpretability for Multiple Instance Learning
Graph Neural Networks with Learnable Structural and Positional Representations
NETWORK INSENSITIVITY TO PARAMETER NOISE VIA PARAMETER ATTACK DURING TRAINING
Online Facility Location with Predictions
When, Why, and Which Pretrained GANs Are Useful?
On the Importance of Difficulty Calibration in Membership Inference Attacks
Multi-Agent MDP Homomorphic Networks
CycleMLP: A MLP-like Architecture for Dense Prediction
When should agents explore?
RotoGrad: Gradient Homogenization in Multitask Learning
Learning a subspace of policies for online adaptation in Reinforcement Learning
Visual hyperacuity with moving sensor and recurrent neural computations
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Fast Differentiable Matrix Square Root
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference
On the Limitations of Multimodal VAEs
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Neural Link Prediction with Walk Pooling
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Discrepancy-Based Active Learning for Domain Adaptation
NASPY: Automated Extraction of Automated Machine Learning Models
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
cosFormer: Rethinking Softmax In Attention
On Distributed Adaptive Optimization with Gradient Compression
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification
iFlood: A Stable and Effective Regularizer
Discovering Latent Concepts Learned in BERT
Learning Multimodal VAEs through Mutual Supervision
Conditioning Sequence-to-sequence Networks with Learned Activations
Implicit Bias of Adversarial Training for Deep Neural Networks
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Case-based reasoning for better generalization in textual reinforcement learning
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Optimal Transport for Causal Discovery
Understanding and Preventing Capacity Loss in Reinforcement Learning
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes
A fast and accurate splitting method for optimal transport: analysis and implementation
FP-DETR: Detection Transformer Advanced by Fully Pre-training
Attacking deep networks with surrogate-based adversarial black-box methods is easy
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
On-Policy Model Errors in Reinforcement Learning
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs
Evaluating Model-Based Planning and Planner Amortization for Continuous Control
Anomaly Detection for Tabular Data with Internal Contrastive Learning
Frame Averaging for Invariant and Equivariant Network Design
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Spike-inspired rank coding for fast and accurate recurrent neural networks
Learning Super-Features for Image Retrieval
Multi-Task Processes
Distributionally Robust Fair Principal Components via Geodesic Descents
GRAND++: Graph Neural Diffusion with A Source Term
Generalization Through the Lens of Leave-One-Out Error
Learning transferable motor skills with hierarchical latent mixture policies
How to Train Your MAML to Excel in Few-Shot Classification
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
(ends 4:30 AM)
4 a.m.
9 a.m.
Oral 3: Learning from distribution shift [9:00-10:30]
Oral s 9:00-10:30
[9:00] Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
[9:15] Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
[9:30] A Fine-Grained Analysis on Distribution Shift
[9:45] Sparse Communication via Mixed Distributions
[10:00] Frame Averaging for Invariant and Equivariant Network Design
[10:15] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
(ends 10:30 AM)
Oral 3: Meta-learning and adaptation [9:00-10:30]
Oral s 9:00-10:30
[9:00] Bootstrapped Meta-Learning
[9:15] Coordination Among Neural Modules Through a Shared Global Workspace
[9:30] Meta-Learning with Fewer Tasks through Task Interpolation
[9:45] Weighted Training for Cross-Task Learning
[10:00] Domino: Discovering Systematic Errors with Cross-Modal Embeddings
[10:15] Extending the WILDS Benchmark for Unsupervised Adaptation
(ends 10:30 AM)
10:30 a.m.
Poster Session 8 [10:30-12:30]
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
A Comparison of Hamming Errors of Representative Variable Selection Methods
Neural Methods for Logical Reasoning over Knowledge Graphs
Auto-scaling Vision Transformers without Training
Topologically Regularized Data Embeddings
Assessing Generalization of SGD via Disagreement
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
Adversarial Unlearning of Backdoors via Implicit Hypergradient
How Attentive are Graph Attention Networks?
Linking Emergent and Natural Languages via Corpus Transfer
Multimeasurement Generative Models
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Graph-Relational Domain Adaptation
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Memorizing Transformers
Pseudo Numerical Methods for Diffusion Models on Manifolds
Capturing Structural Locality in Non-parametric Language Models
Learning Prototype-oriented Set Representations for Meta-Learning
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
Label Leakage and Protection in Two-party Split Learning
Fortuitous Forgetting in Connectionist Networks
PAC-Bayes Information Bottleneck
Bayesian Framework for Gradient Leakage
Plant 'n' Seek: Can You Find the Winning Ticket?
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Differentiable DAG Sampling
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Knowledge Infused Decoding
The Information Geometry of Unsupervised Reinforcement Learning
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
Offline Reinforcement Learning with Value-based Episodic Memory
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Cross-Domain Imitation Learning via Optimal Transport
Poisoning and Backdooring Contrastive Learning
Space-Time Graph Neural Networks
On the Certified Robustness for Ensemble Models and Beyond
Gaussian Mixture Convolution Networks
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
Phenomenology of Double Descent in Finite-Width Neural Networks
Transformer-based Transform Coding
Orchestrated Value Mapping for Reinforcement Learning
Information Prioritization through Empowerment in Visual Model-based RL
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
Better Supervisory Signals by Observing Learning Paths
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings
Learning Distributionally Robust Models at Scale via Composite Optimization
No One Representation to Rule Them All: Overlapping Features of Training Methods
Value Gradient weighted Model-Based Reinforcement Learning
Data Poisoning Won’t Save You From Facial Recognition
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
Task-Induced Representation Learning
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
Understanding and Leveraging Overparameterization in Recursive Value Estimation
Fast Regression for Structured Inputs
Practical Conditional Neural Process Via Tractable Dependent Predictions
On the Importance of Firth Bias Reduction in Few-Shot Classification
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Generalized rectifier wavelet covariance models for texture synthesis
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
FILIP: Fine-grained Interactive Language-Image Pre-Training
Comparing Distributions by Measuring Differences that Affect Decision Making
Understanding over-squashing and bottlenecks on graphs via curvature
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Missingness Bias in Model Debugging
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
Learning to Dequantise with Truncated Flows
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation
Memory Augmented Optimizers for Deep Learning
RvS: What is Essential for Offline RL via Supervised Learning?
A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks
Step-unrolled Denoising Autoencoders for Text Generation
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
CoMPS: Continual Meta Policy Search
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
Learning State Representations via Retracing in Reinforcement Learning
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction
Churn Reduction via Distillation
On the role of population heterogeneity in emergent communication
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Consistent Counterfactuals for Deep Models
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
DIVA: Dataset Derivative of a Learning Task
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Distribution Compression in Near-Linear Time
Long Expressive Memory for Sequence Modeling
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
Image BERT Pre-training with Online Tokenizer
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
Diverse Client Selection for Federated Learning via Submodular Maximization
On Improving Adversarial Transferability of Vision Transformers
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions
CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
The Efficiency Misnomer
(ends 12:30 PM)
noon
5 p.m.
Invited Talk:
Accelerating AI Systems: Let the Data Flow!
Kunle Olukotun
(ends 6:15 PM)
6:30 p.m.
Poster Session 9 [6:30-8:30]
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
Online Hyperparameter Meta-Learning with Hypergradient Distillation
EViT: Expediting Vision Transformers via Token Reorganizations
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL
Learning Weakly-supervised Contrastive Representations
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies
Stein Latent Optimization for Generative Adversarial Networks
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization
Learned Simulators for Turbulence
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
SGD Can Converge to Local Maxima
Large Language Models Can Be Strong Differentially Private Learners
Pre-training Molecular Graph Representation with 3D Geometry
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Path Auxiliary Proposal for MCMC in Discrete Space
Neural Solvers for Fast and Accurate Numerical Optimal Control
Optimization inspired Multi-Branch Equilibrium Models
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
A Theory of Tournament Representations
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
Pretrained Language Model in Continual Learning: A Comparative Study
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation
Convergent and Efficient Deep Q Learning Algorithm
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
Concurrent Adversarial Learning for Large-Batch Training
Symbolic Learning to Optimize: Towards Interpretability and Scalability
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
Meta-Imitation Learning by Watching Video Demonstrations
Context-Aware Sparse Deep Coordination Graphs
Scale Efficiently: Insights from Pretraining and Finetuning Transformers
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Benchmarking the Spectrum of Agent Capabilities
Declarative nets that are equilibrium models
BEiT: BERT Pre-Training of Image Transformers
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
TRGP: Trust Region Gradient Projection for Continual Learning
Generalizing Few-Shot NAS with Gradient Matching
Learning Continuous Environment Fields via Implicit Functions
On the Convergence of Certified Robust Training with Interval Bound Propagation
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Half-Inverse Gradients for Physical Deep Learning
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Learning Versatile Neural Architectures by Propagating Network Codes
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Convergent Graph Solvers
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Zero-Shot Self-Supervised Learning for MRI Reconstruction
Neural Networks as Kernel Learners: The Silent Alignment Effect
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation
Exploring extreme parameter compression for pre-trained language models
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Tracking the risk of a deployed model and detecting harmful distribution shifts
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
(ends 8:30 PM)
8 p.m.
THU 28 APR
1 a.m.
Oral 4: Sequence modeling [1:00-2:30]
Oral s 1:00-2:30
[1:00] Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
[1:15] Natural Language Descriptions of Deep Features
[1:30] Finetuned Language Models are Zero-Shot Learners
[1:45] Large Language Models Can Be Strong Differentially Private Learners
[2:00] GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
[2:15] Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
(ends 2:30 AM)
Oral 4: Probablistic Models, Vision [1:00-2:30]
Oral s 1:00-2:30
[1:00] Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
[1:15] Comparing Distributions by Measuring Differences that Affect Decision Making
[1:30] Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling
[1:45] RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
[2:00] BEiT: BERT Pre-Training of Image Transformers
[2:15] Resolving Training Biases via Influence-based Data Relabeling
(ends 2:30 AM)
2:30 a.m.
Poster Session 10 [2:30-4:30]
CURVATURE-GUIDED DYNAMIC SCALE NETWORKS FOR MULTI-VIEW STEREO
Learning to Extend Molecular Scaffolds with Structural Motifs
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS
Policy improvement by planning with Gumbel
Deep Point Cloud Reconstruction
How Do Vision Transformers Work?
GNN-LM: Language Modeling based on Global Contexts via GNN
A Deep Variational Approach to Clustering Survival Data
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Nonlinear ICA Using Volume-Preserving Transformations
Uncertainty Modeling for Out-of-Distribution Generalization
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Group equivariant neural posterior estimation
AlphaZero-based Proof Cost Network to Aid Game Solving
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Generative Pseudo-Inverse Memory
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Variational Predictive Routing with Nested Subjective Timescales
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Evaluating Disentanglement of Structured Representations
Exploiting Class Activation Value for Partial-Label Learning
Omni-Dimensional Dynamic Convolution
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
Dynamic Token Normalization improves Vision Transformers
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
Prototypical Contrastive Predictive Coding
Dive Deeper Into Integral Pose Regression
Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration
CKConv: Continuous Kernel Convolution For Sequential Data
Understanding approximate and unrolled dictionary learning for pattern recovery
On the Pitfalls of Analyzing Individual Neurons in Language Models
IGLU: Efficient GCN Training via Lazy Updates
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
CoBERL: Contrastive BERT for Reinforcement Learning
Differentiable Expectation-Maximization for Set Representation Learning
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
Vitruvion: A Generative Model of Parametric CAD Sketches
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Explaining Point Processes by Learning Interpretable Temporal Logic Rules
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Universal Approximation Under Constraints is Possible with Transformers
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Self-supervised Learning is More Robust to Dataset Imbalance
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning
Counterfactual Plans under Distributional Ambiguity
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
Robust and Scalable SDE Learning: A Functional Perspective
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
Adversarially Robust Conformal Prediction
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation
Constraining Linear-chain CRFs to Regular Languages
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
Representation-Agnostic Shape Fields
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize
Defending Against Image Corruptions Through Adversarial Augmentations
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis
Exploring Memorization in Adversarial Training
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
AS-MLP: An Axial Shifted MLP Architecture for Vision
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
It Takes Two to Tango: Mixup for Deep Metric Learning
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data
Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
(ends 4:30 AM)
4 a.m.
6 a.m.
9 a.m.
From Reinforcement Learning to AI:
Doina Precup
Doina Precup
(ends 10:15 AM)
10:30 a.m.
Poster Session 11 [10:30-12:30]
Message Passing Neural PDE Solvers
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Bag of Instances Aggregation Boosts Self-supervised Distillation
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics
Non-Linear Operator Approximations for Initial Value Problems
Energy-Inspired Molecular Conformation Optimization
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
Sparse Communication via Mixed Distributions
Imitation Learning from Observations under Transition Model Disparity
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Learning Object-Oriented Dynamics for Planning from Text
Attention-based Interpretability with Concept Transformers
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
Monotonic Differentiable Sorting Networks
Learning Curves for SGD on Structured Features
Domain Adversarial Training: A Game Perspective
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Online Ad Hoc Teamwork under Partial Observability
Compositional Attention: Disentangling Search and Retrieval
Boosting Randomized Smoothing with Variance Reduced Classifiers
Representation Learning for Online and Offline RL in Low-rank MDPs
On Bridging Generic and Personalized Federated Learning for Image Classification
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
Bregman Gradient Policy Optimization
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Looking Back on Learned Experiences For Class/task Incremental Learning
An Agnostic Approach to Federated Learning with Class Imbalance
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
Local Feature Swapping for Generalization in Reinforcement Learning
Equivariant Subgraph Aggregation Networks
Learning-Augmented $k$-means Clustering
Learning to Map for Active Semantic Goal Navigation
Query Embedding on Hyper-Relational Knowledge Graphs
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
Pix2seq: A Language Modeling Framework for Object Detection
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Language model compression with weighted low-rank factorization
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
Minimax Optimization with Smooth Algorithmic Adversaries
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
A Zest of LIME: Towards Architecture-Independent Model Distances
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Pareto Policy Adaptation
Unified Visual Transformer Compression
Can an Image Classifier Suffice For Action Recognition?
On the relation between statistical learning and perceptual distances
Explanations of Black-Box Models based on Directional Feature Interactions
An Operator Theoretic View On Pruning Deep Neural Networks
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Optimization and Adaptive Generalization of Three layer Neural Networks
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems
Learning more skills through optimistic exploration
Conditional Object-Centric Learning from Video
Autonomous Reinforcement Learning: Formalism and Benchmarking
Mirror Descent Policy Optimization
Understanding the Variance Collapse of SVGD in High Dimensions
FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning
Learning to Schedule Learning rate with Graph Neural Networks
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
Anti-Concentrated Confidence Bonuses For Scalable Exploration
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
EntQA: Entity Linking as Question Answering
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Towards a Unified View of Parameter-Efficient Transfer Learning
Properties from mechanisms: an equivariance perspective on identifiable representation learning
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions
Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs
FILM: Following Instructions in Language with Modular Methods
R4D: Utilizing Reference Objects for Long-Range Distance Estimation
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Proof Artifact Co-Training for Theorem Proving with Language Models
Language-biased image classification: evaluation based on semantic representations
Practical Integration via Separable Bijective Networks
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Possibility Before Utility: Learning And Using Hierarchical Affordances
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Increasing the Cost of Model Extraction with Calibrated Proof of Work
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lossless Compression with Probabilistic Circuits
Programmatic Reinforcement Learning without Oracles
Overcoming The Spectral Bias of Neural Value Approximation
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Deep ReLU Networks Preserve Expected Length
(ends 12:30 PM)
noon
12:30 p.m.
Town Hall:
Town Hall
(ends 1:30 PM)
2 p.m.
5 p.m.
Invited Talk:
Petascale connectomics and beyond
H. Sebastian Seung
(ends 6:15 PM)
6:30 p.m.
Poster Session 12 [6:30-8:30]
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences
Extending the WILDS Benchmark for Unsupervised Adaptation
On the benefits of maximum likelihood estimation for Regression and Forecasting
Hybrid Random Features
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Surrogate Gap Minimization Improves Sharpness-Aware Training
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Neural Parameter Allocation Search
Discriminative Similarity for Data Clustering
Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation
SketchODE: Learning neural sketch representation in continuous time
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Model-augmented Prioritized Experience Replay
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Few-Shot Backdoor Attacks on Visual Object Tracking
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training
Transferable Adversarial Attack based on Integrated Gradients
Understanding Domain Randomization for Sim-to-real Transfer
Reliable Adversarial Distillation with Unreliable Teachers
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Generalized Demographic Parity for Group Fairness
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Lipschitz-constrained Unsupervised Skill Discovery
Provable Learning-based Algorithm For Sparse Recovery
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Enhancing Cross-lingual Transfer by Manifold Mixup
Model-Based Offline Meta-Reinforcement Learning with Regularization
Huber Additive Models for Non-stationary Time Series Analysis
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
MT3: Multi-Task Multitrack Music Transcription
Relational Surrogate Loss Learning
Neural Spectral Marked Point Processes
A General Analysis of Example-Selection for Stochastic Gradient Descent
Topological Experience Replay
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
OntoProtein: Protein Pretraining With Gene Ontology Embedding
KL Guided Domain Adaptation
Acceleration of Federated Learning with Alleviated Forgetting in Local Training
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Interacting Contour Stochastic Gradient Langevin Dynamics
Self-Joint Supervised Learning
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
Few-shot Learning via Dirichlet Tessellation Ensemble
Natural Language Descriptions of Deep Features
Shuffle Private Stochastic Convex Optimization
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
Universalizing Weak Supervision
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
Vector-quantized Image Modeling with Improved VQGAN
Adversarial Robustness Through the Lens of Causality
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
Do deep networks transfer invariances across classes?
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
Constructing Orthogonal Convolutions in an Explicit Manner
QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Improving the Accuracy of Learning Example Weights for Imbalance Classification
InfinityGAN: Towards Infinite-Pixel Image Synthesis
Gradient Matching for Domain Generalization
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning
Model Zoo: A Growing Brain That Learns Continually
LoRA: Low-Rank Adaptation of Large Language Models
(ends 8:30 PM)
FRI 29 APR
midnight
Workshop:
Workshop on the Elements of Reasoning: Objects, Structure and Causality
(ends 11:50 AM)
Remarks:
Closing Remarks
(ends 12:30 AM)
1:45 a.m.
Workshop:
Workshop on Agent Learning in Open-Endedness
(ends 1:15 PM)
2 a.m.
Workshop:
AfricaNLP 2022: NLP for African languages
(ends 11:30 AM)
3 a.m.
Workshop:
Wiki-M3L: Wikipedia and Multimodal & Multilingual Research
(ends 1:20 PM)
5 a.m.
Workshop:
Emergent Communication: New Frontiers
(ends 2:00 PM)
Workshop:
Setting up ML Evaluation Standards to Accelerate Progress
(ends 3:35 PM)
Workshop:
From Cells to Societies: Collective Learning Across Scales
(ends 3:05 PM)
Workshop:
Gamification and Multiagent Solutions
(ends 3:00 PM)
Workshop:
Geometrical and Topological Representation Learning
(ends 2:00 PM)
Workshop:
Deep Learning for Code
(ends 2:15 PM)
5:45 a.m.
Workshop:
GroundedML: Anchoring Machine Learning in Classical Algorithmic Theory
(ends 2:30 PM)
Workshop:
Socially Responsible Machine Learning
(ends 3:00 PM)
6 a.m.
Workshop:
Deep Learning on Graphs for Natural Language Processing
(ends 3:00 PM)
Workshop:
Machine Learning for Drug Discovery (MLDD)
(ends 2:30 PM)
Workshop:
3rd Workshop on practical ML for Developing Countries: learning under limited/low resource scenarios
(ends 12:45 PM)
Workshop:
Deep Generative Models for Highly Structured Data
(ends 12:50 PM)
8 a.m.
Workshop:
Generalizable Policy Learning in the Physical World
(ends 6:30 PM)
Workshop:
CoSubmitting Summer (CSS) Workshop
(ends 2:00 PM)
Workshop:
AI for Earth and Space Science
(ends 4:35 PM)
9 a.m.
Workshop:
PAIR^2Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data
(ends 6:00 PM)