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Poster
Mon 1:00 Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron Courville, Zhanxing Zhu
Poster
Mon 1:00 Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou, Roderick Murray-Smith
Poster
Mon 9:00 Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
Joshua Chang, Patrick A Fletcher, Jungmin Han, Ted Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson Chow
Poster
Mon 9:00 A statistical theory of cold posteriors in deep neural networks
Laurence Aitchison
Poster
Tue 1:00 Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Asish Ghoshal, Xilun Chen, Sonal Gupta, Luke Zettlemoyer, Yashar Mehdad
Poster
Tue 1:00 Bayesian Context Aggregation for Neural Processes
Michael Volpp, Fabian Flürenbrock, Lukas Grossberger, Christian Daniel, Gerhard Neumann
Poster
Tue 1:00 Activation-level uncertainty in deep neural networks
Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández Lobato
Poster
Tue 9:00 Scalable Bayesian Inverse Reinforcement Learning
Alex Chan, Mihaela van der Schaar
Poster
Tue 9:00 Getting a CLUE: A Method for Explaining Uncertainty Estimates
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández Lobato
Poster
Tue 9:00 Uncertainty-aware Active Learning for Optimal Bayesian Classifier
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian
Poster
Tue 17:00 FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen, Wei-Lun Chao
Poster
Tue 17:00 A Discriminative Gaussian Mixture Model with Sparsity
Hideaki Hayashi, Seiichi Uchida
Poster
Wed 1:00 Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
Yu Cheng, Honghao Lin
Poster
Wed 1:00 Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne
Poster
Wed 1:00 Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Alihan Hüyük, Dan Jarrett, Cem Tekin, Mihaela van der Schaar
Oral
Wed 4:05 Getting a CLUE: A Method for Explaining Uncertainty Estimates
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández Lobato
Spotlight
Wed 5:35 Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron Courville, Zhanxing Zhu
Poster
Wed 9:00 Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Paul Pu Liang, Manzil Zaheer, Yuan Wang, Amr Ahmed
Poster
Wed 9:00 Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer
Poster
Wed 9:00 Property Controllable Variational Autoencoder via Invertible Mutual Dependence
Xiaojie Guo, Yuanqi Du, Liang Zhao
Poster
Wed 9:00 Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Martin Wistuba, Josif Grabocka
Poster
Wed 9:00 Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek
Poster
Wed 9:00 Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Raissa KAMENI KAMENI, Toniann Pitassi, Richard Zemel
Poster
Wed 17:00 A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Renjie Liao, Raquel Urtasun, Richard Zemel
Poster
Wed 17:00 AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini, Arvind Krishnamurthy
Poster
Thu 1:00 Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
Poster
Thu 1:00 An Unsupervised Deep Learning Approach for Real-World Image Denoising
Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao
Poster
Thu 1:00 Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory
Jason Ramapuram, Yan Wu, Alexandros Kalousis
Poster
Thu 9:00 Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Jake Snell, Richard Zemel
Poster
Thu 17:00 Learning to Make Decisions via Submodular Regularization
Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen
Poster
Thu 17:00 DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation
Minjia Zhang, Menghao Li, Chi Wang, Mingqin Li
Workshop
Fri 7:04 Poster Spotlight "Overfitting in Bayesian Optimization: an empirical study and early-stopping solution"
Huibin Shen, Anastasia Makarova
Workshop
Fri 7:10 Poster Spotlight "Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure"
Zhijie Deng
Workshop
Fri 8:30 Break + Posters
Workshop
Fri 12:15 A Bayesian Optimization Approach to Estimating Expected Match Time and Organ Quality in Kidney Exchange
Naveen Durvasula