Workshops
Workshop
[ Hall 4 #2 ]
Abstract
Datasets are a central pillar of machine learning (ML) research—from pretraining to evaluation and benchmarking. However, a growing body of work highlights serious issues throughout the ML data ecosystem, including the under-valuing of data work, ethical issues in datasets that go undiscovered, a lack of standardized dataset deprecation procedures, the (mis)use of datasets out-of-context, an overemphasis on single metrics rather than holistic model evaluation, and the overuse of the same few benchmark datasets. Thus, developing guidelines, goals, and standards for data practices is critical; beyond this, many researchers have pointed to a need for a more fundamental culture shift surrounding data and benchmarking in ML. At present it is not clear how to mobilize the ML community for such a transformation. In this workshop, we aim to explore this question, including by examining the role of data repositories in the ML data landscape. These repositories have received relatively little attention in this context, despite their key role in the storage, documentation, and sharing of ML datasets. We envision that these repositories, as central purveyors of ML datasets, have the potential to instigate far-reaching changes to ML data and benchmarking culture via the features they implement and the standards they enforce …
Workshop
[ Peridot 202-203 ]
Abstract
Accessing large scale and high quality data has been shown to be one of the most important factors to the performance of machine learning models. Recent works show that large (language) models can greatly benefit from training with massive data from diverse (domain specific) sources and aligning with user intention. However, the use of certain data sources can trigger privacy, fairness, copyright, and safety concerns. The impressive performance of generative artificial intelligence popularized the usage of synthetic data, and many recent works suggest (guided) synthesization can be useful for both general purpose and domain specific applications. For example, Yu et al. 2024, Xie et al. 2024, Hou et al. 2024 demonstrate promising preliminary results in synthesizing private-like data, while Wu et al. 2024 highlight existing gaps and challenges. As techniques like self-instruct (Wang et al. 2021) and self-alignment (Li et al. 2024) gain traction, researchers are questioning the implications of synthetic data (Alemohammad et al. 2023, Dohmatob et al. 2024, Shumailov et al. 2024). Will synthetic data ultimately solve the data access problem for machine learning? This workshop seeks to address this question by highlighting the limitations and opportunities of synthetic data. It aims to bring together researchers working on …
Workshop
[ Peridot 201&206 ]
Abstract
Machine learning (ML) models are impressive when they work but they can also show unreliable, untrustworthy, and harmful dangerous behavior. Such behavior is even more common in the era of large models, such as chatGPT, which are quickly being adopted even though we do not understand why they work so well and fail miserably at times. Unfortunately, such rapid dissemination encourages irresponsible use, for example, to spread misinformation or create deep fakes, while hindering the efforts to use them to solve pressing societal problems and advance human knowledge. Ideally, we want models that have a human-like capacity to learn by observing, theorizing, and validating the theories to improve the understanding of the world. At the very least, we want them to aid human knowledge and help us to further enrich it. Our goal in this workshop is to bring together researchers working on understanding model behavior and show how this key aspect can lead to discovering new human knowledge. The workshop will include theoretical topics on understanding model behavior, namely interpretability and explainability (XAI), but also three distinct scientific application areas: weather and climate, healthcare, and material science (ML4Science).
Workshop
[ Garnet 218-219 ]
Abstract
This workshop explores the intersection of scale-driven generative artificial intelligence (AI) and the correctness-focused principles of verification. Formal analysis tools such as theorem provers, satisfiability solvers, and execution monitoring have demonstrated success in ensuring properties of interest across a range of tasks in software development and mathematics where precise reasoning is necessary. However, these methods face scaling challenges. Recently, generative AI such as large language models (LLMs) has been explored as a scalable and adaptable option to create solutions in these settings. The effectiveness of AI in these settings increases with more compute and data, but unlike traditional formalisms, they are built around probabilistic methods – not correctness by construction. In the VerifAI: AI Verification in the Wild workshop we invite papers and discussions that discuss how to bridge these two fields. Potential angles include, but are not limited to the following: generative AI for formal methods, formal methods for generative AI, AI as verifiers, datasets and benchmarks, and a special theme: LLMs for code generation. We welcome novel methodologies, analytic contributions, works in progress, negative results, andreview and positional papers that will foster discussion. We will also have a track for tiny or short papers.
Workshop
[ Garnet 213-215 ]
Abstract
Our limited understanding of the biological mechanisms underlying diseases remains a critical bottleneck in drug discovery. As a result, we often lack insights into why patients develop specific conditions, leading to the failure of many drug candidates in clinical trials. Recent advancements in genomics platforms and the emergence of diverse omics datasets have sparked increasing interest in this field. The primary objective of this workshop is to bridge the gap between machine learning and genomics, emphasizing target identification and emerging drug modalities such as gene and cell therapies and RNA-based drugs. By fostering interdisciplinary collaboration, we aim to advance the integration of these disciplines and accelerate innovation in drug discovery.
Workshop
[ Garnet 216-218 ]
Abstract
As foundation models (FMs) scale, they face a data bottleneck, where the growth of high-quality internet data unable to keep pace with their training needs. This is most apparent with text data already, has been a consistent problem in domains such as embodied intelligence, and is expected to soon inflict other modalities as well. ***Self-improvement***, a paradigm where models generate and train on synthetic data generated from the same or other models, offers a promising solution. This paradigm differs from both supervised learning, which relies on curated human data, and reinforcement learning (RL), which depends on external rewards. Self-improvement frameworks require models to self-curate training data, often using imperfect learned verifiers, with unique challenges. This workshop will explore algorithms for self-improvement, covering topics such as synthetic data, multi-agent and multi-modal systems, weak-to-strong generalization, inference-time self-supervision, and theoretical limits.
Workshop
[ Opal 103-104 ]
Abstract
Machine learning for remote sensing (ML4RS) has emerged as a critical and exciting area of research, with the potential to address some of the most pressing global challenges, including climate change, food security, disaster management, and conservation. Remote sensing data, collected from diverse instruments capturing the Earth across various spatial, temporal, and spectral dimensions, offers unique research opportunities and challenges for the ML community. Unlike traditional data modalities, these datasets are high-dimensional, extremely multi-modal, and contain patterns at a multitude of spatial and temporal scales. These characteristics often require specialized approaches in cross-cutting ML topics like self-supervised/semi-supervised learning, domain adaptation/generalization, and multi-modal learning/data fusion to unlock their full potential. Our workshop will foster discussion and feedback on early-stage work that is critical to impactful applications and new developments in machine learning for remote sensing.
Workshop
[ Hall 4 #3 ]
Abstract
The increasing complexity of modern machine learning models exposes the limitations of the traditional, monolithic approach to their development, raising concerns about cost and sustainability.This workshop challenges this approach by advocating for a new paradigm based on modular design and functional specialization. Inspired by principles from software engineering, we envision a future where models are composed of independently trainable modules, enabling asynchronous development, incremental updates, and cross-task generalization through composability. This shift towards modularity unlocks new possibilities for collaborative model development where researchers can contribute specialized modules, combine existing models, and participate in decentralized training schemes. By embracing modularity, we can democratize deep learning research, enabling smaller teams and institutions to contribute to the development of powerful and efficient models. Furthermore, modularity promises to enhance model interpretability, and maintainability, paving the way for more robust and efficient AI systems. This workshop aims to accelerate this transition towards a more collaborative and sustainable future for deep learning.
Workshop
[ Hall 4 #1 ]
Abstract
Watermarking involves embedding a hidden signal into digital media like text, images, and audio to establish ownership or ensure authenticity. It has become increasingly important in the age of generative AI. However, despite its growing significance, watermarking in the AI community often gets lost in broader conversations around adversarial robustness, and general security, and safety. We argue that watermarking needs its own dedicated space in AI conferences for discussion and exploration, where researchers can dig deeper into the technical specifics of this field and build on a foundation of research spanning over 20 years. The aim of this workshop is to bring together experts from academia, industry, policy and from different communities to discuss advancements and challenges in watermarking technologies. The event will facilitate the exchange of ideas and collaborative problem-solving.
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<br>Topics of interest include, but are not limited to:
<br>- Algorithmic Advances: Multi-modal watermarking, model watermarking, dataset tracing and attribution.
<br>- Watermark Security: Theoretical results on strong watermark impossibility, black and white-box adversarial attacks, advanced threat models, open-sourced and publicly detectable watermarking, and zero-knowledge watermarking.
<br>- Evaluation: Benchmarks for watermarking, perceptual models and watermark-specific quality evaluation metrics, and bias in watermarking robustness.
<br>- Industry Requirements: Large bit watermarking, low …
Workshop
[ Hall 1 Apex ]
Abstract
This workshop aims to build a multidisciplinary research community around the emerging field of human-AI coevolution (HAIC) to understand the feedback loops that emerge from continuous and long-term human-AI interaction. As AI systems have become more prevalent and have been present in society over longer periods, scholars from diverse fields and methodologies have come to focus on HAIC and its importance for system architecture, human feedback, regulation, and other domains. Through this workshop we hope to lay a collaborative foundation for this research agenda. To achieve this we will organize expert talks from academia and industry, dynamic panel discussions, interactive breakout sessions, and networking opportunities, drawing on our diverse experience organizing related workshops at leading conferences in ML, NLP, HCI, and related fields.
Workshop
[ Hall 4 #5 ]
Abstract
This workshop will discuss the latest multidisciplinary developments in Associative Memory. A number of leading researchers in this topic from around the world have already agreed to attend and present their latest results. We anticipate sharing their presentations and outlining future research directions in this emerging field with the rest of the ICLR community.
Workshop
[ Topaz 220-225 ]
Abstract
The ongoing deep learning revolution of the last decade has brought about hundreds of millions of neural networks (NNs) trained on diverse datasets.At the same time, the recent rise of foundation models has led to a rapid increase in the number of publicly available neural network models. On Hugging Face alone, there are over a million models, with thousands more added daily. As a result, the ample knowledge contained in the data, the abstraction learned via training, as well the trained models' behaviours themselves are stored in the architectures and parameters of trained NNs. Despite this massive growth, little research has been conducted into processing model weights, and they are rarely considered a data modality. This workshop aims to create a community around Weight Space Learning by bringing together the scattered sub-communities that already interface with model weights, with the ultimate goal of democratizing model weights as a proper data modality.
Workshop
[ Garnet 212&219 ]
Abstract
The year 2024 has seen an explosion of interest in humanoid robots. In the 7th Robot Learning workshop, to be held at ICLR-2025, we will look beyond the humanoid embodiment and ask: how far are we from robots with human-level abilities? What do we need to improve about embodied learning, decision-making, perception, and data collection to train generally physically capable robots to robustly perform a wide range of activities such as cooking or tidying up the house -- activities that people do without much thinking? We believe many of the weaknesses of the current robotic systems to be a reflection of the shortcomings of general AI methods and models. As such, in this workshop we will seek diverse perspectives from robotics-focused and robotics-orthogonal parts of the ICLR community alike, scientific contributions from academia and industry, as well as participants from a variety of backgrounds and career stages. Capitalizing on our prior experience with robotics showcases, in keeping with the spirit of the times we will solicit several humanoid robotics companies to exhibit their robots during the workshop's poster sessions.
Workshop
[ Topaz Concourse ]
Abstract
How can we trust large language models (LLMs) when they generate text with confidence, but sometimes hallucinate or fail to recognize their own limitations? As foundation models like LLMs and multimodal systems become pervasive across high-stakes domains—from healthcare and law to autonomous systems—the need for uncertainty quantification (UQ) is more critical than ever. Uncertainty quantification provides a measure of how much confidence a model has in its predictions or generations, allowing users to assess when to trust the outputs and when human oversight may be needed. This workshop aims to focus on the question of UQ and hallucination in the modern LLMs and multimodal systems and explore the open questions in the domain.
Workshop
[ Conference GHJ ]
Abstract
Some of the most exciting and impactful open scientific problems have computational complexity as the limiting factor to an in silico solution, e. g. high–temperature superconductivity and fusion power. Atoms behave according to the well–established laws of quantum mechanics, but as system size grows computations quickly become intractable. This workshop will gather for cross–pollination a diverse group of researchers belonging to difference scientific domains and machine learning approaches. The immediate outcome will be an exchange of ideas, datasets, and crystallized problem statements, all towards the ultimate goal of developing universal AI methods that would be able find efficient and accurate approximations of complex systems from low-level theory. If we solve scale transition, we solve science.
Workshop
[ Hall 4 #7 ]
Abstract
Large Language Models (LLMs) have emerged as transformative tools in both research and industry, excelling across a wide array of tasks. However, their growing computational demands especially during inference—raise significant concerns about accessibility, environmental sustainability, and deployment feasibility. At the same time, sparsity-based techniques are proving critical not just for improving efficiency but also for enhancing interpretability, modularity, and adaptability in AI systems. This workshop aims to bring together researchers and practitioners from academia and industry who are advancing the frontiers of sparsity in deep learning. Our scope spans several interrelated topics, including Mixture of Experts (MoEs), LLM inference and serving, network pruning, sparse training, distillation, activation sparsity, low-rank adapters, hardware innovations and quantization. A key objective is to foster connections and unlock synergies between traditionally independent yet highly related research areas, such as activation sparsity and sparse autoencoders (SAEs), or quantization and KV cache compression. Rather than focusing solely on efficiency, we aim to explore how sparsity can serve as a unifying framework across multiple dimensions of AI—driving advances in interpretability, generalization, and system design. By facilitating the fusion of ideas from different topics, the workshop will create new opportunities for innovation. We encourage participants to think beyond traditional …
Workshop
[ Peridot 204-205 ]
Abstract
Current AI research and applications often prioritize adult-focused solutions, while progress in AI designed specifically for children's development, health, and education has lagged behind. Our workshop aims to spotlight this issue and bring together researchers from diverse fields to discuss the future of AI design and its applications for children.In the era of AI, developing bespoke AI systems for children holds special significance:(i) Advanced AI technologies, such as large language models (LLMs), have the potential to support children’s development, education, and mental health, posing a critical new frontier for research.(ii) AI in pediatric healthcare is essential, as early diagnosis of childhood diseases can lead to timely interventions, improving prognoses and reducing infant mortality rates.(iii) AI can also provide valuable tools helping children in low-resource countries, helping bridge gaps in education, healthcare, and other developmental supports.This workshop will invite researchers from the fields of AI, child psychology, education, pediatrics and social good to discuss how AI, particularly new generative models like LLMs, can address the unique challenges in pediatrics, child psychology, and education. We will also explore the potential risks associated with AI applications for children.The insights from the workshop's panel discussions will be summarized in a survey paper and submitted …
Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation
Workshop
[ Opal 101-102 ]
Abstract
AI models for science have the potential to harness large datasets, accelerate scientific discoveries, and transform numerous fields. Through this workshop, our mission is to foster interdisciplinary collaboration to develop fully autonomous AI systems, addressing challenges like benchmark datasets, human-AI collaboration, robust tools and methods for validating AI outputs, and trustworthiness. By tackling these issues, we can unlock AI's transformative potential in research. In this workshop, themed Agentic AI for Science, we will explore these critical topics and welcome diverse perspectives. We will focus on integrating agentic AI systems to enhance scientific discovery while upholding rigorous standards. For AI to contribute effectively, it must generate novel hypotheses, comprehend their applications, quantify testing resources, and validate feasibility through well-designed experiments. This workshop serves as a vital forum for collaboration and knowledge-sharing aimed at redefining the landscape of scientific discovery.
Workshop
[ Hall 4 #6 ]
Abstract
In the era of AI-driven transformations, foundation models (FMs) have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities,reshape the future of scientific research and the broader human society, but also introduce challenges intheir in-the-wild/real-world deployments. The 2nd Workshop on FMs in the Wild delves into the urgent need forthese models to be useful when deployed in our societies. The significance of this topic cannot be overstated,as the real-world implications of these models impact everything from daily information access to criticaldecision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeplyabout this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of many properties, including adaptivity, reliability, efficiency, and reasoning ability. Some of thefundamental questions that this workshop aims to address are:1. In-the-wild Adaptation: How can we leverage techniques such as Retrieval-Augmented Generation(RAG), In-context Learning (ICL), or Fine-tuning (FT) to adapt FMs for specific domains, such asdrug discovery, education, or clinical health?2. Reasoning and Planning: How can FMs be enhanced to tackle more complex in-the-wild tasks thatrequire multi-step reasoning or decision-making, such as multi-hop question answering, mathematicalproblem-solving, theorem proving, code generation, or robot planning …
Workshop
[ Hall 4 #4 ]
Abstract
Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.
Workshop
[ Garnet 214-215 ]
Abstract
Despite the remarkable advancements towards generalizability and autonomy in AI systems, persistent challenges such as spurious correlations and shortcut learning continue to hinder the robustness, reliability, and ethical deployment of machine learning systems. These challenges arise from the statistical nature of machine learning algorithms and their implicit or inductive biases at all stages, including data preprocessing, architectures, and optimization. As a result, models rely on spurious patterns rather than understanding underlying causal relationships, making them vulnerable to failure in real-world scenarios where data distributions involve under-represented groups or minority populations. The foundational nature and widespread occurrence of reliance on spurious correlations and shortcut learning make it an important research topic and a gateway to understanding how deep models learn patterns and the underlying mechanisms responsible for their effectiveness and generalization. This workshop aims to foster a collaborative community to address these critical issues by bringing together experts from diverse fields and pushing the boundaries of current research. We will focus on promoting three key avenues: (i) the development of comprehensive evaluation benchmarks and the exploration of under-examined facets of the problem, (ii) the creation of novel solutions for building robust models that effectively tackle spurious correlations in real-world applications, and …
Workshop
[ Hall 4 #7 ]
Abstract
Learning Meaningful Representations of Life 2025 (LMRL 2025) aims to address the growing interest in large-scale representation learning for biological data, driven by the availability of large biological datasets, such as DNA and RNA sequences, protein structures, and cell imaging. There have been many recent papers proposing “foundation models” for biological data, but the performance of these models varies dramatically across domains: in some settings, large-scale pre-training has significantly expanded the range of solvable tasks, while in others, foundation models are often outperformed by simple baselines. This workshop will encourage work that explains this gap by focusing on two key issues: first, identifying the data, models, and algorithms necessary to extract meaningful representations that generalize well to downstream tasks, and second, establishing appropriate methods to evaluate the quality and utility of these learned representations. By bringing together researchers from AI and biology, the workshop aims to foster collaboration, promote standardization of datasets and evaluation metrics, and explore real-world applications that can benefit from improved strategies in representation learning.
Workshop
[ Opal 103-104 ]
Abstract
In recent years, the AI community has made significant strides in protein research, particularly since the breakthrough of AlphaFold2, which has led to advancements in structural biology and drug discovery. The success achieved on proteins gives hope to achieve comparable success on nucleic acids, RNA and DNA. The proposed workshop aims to highlight the unique challenges and possibilities of applying AI to nucleic acids. While advances in RNA structure prediction and nucleic acid language models show promise, the field lags behind proteins in the scale and quality of data and predictive accuracy. Addressing these challenges will drive critical applications in diagnostics, therapeutics, and biotechnology, such as mRNA therapeutics design, RNA-targeting small molecules, and improved genetic variant calling. Furthermore, there is space for advancement in reconstructing complex genomes, such as cancer or plant genomes, and detecting and understanding epigenetic and epitranscriptomic modifications. By bringing together AI researchers and domain experts in nucleic acids at the ICLR workshop, we aim to foster collaborations that advance the role of AI in nucleic acid research, ultimately pushing the boundaries of what AI can achieve in understanding and manipulating life’s fundamental molecules.
Workshop
[ Topaz Concourse ]
Abstract
We propose a full-day, medium-sized workshop at ICLR 2025 titled “AI for Accelerated Materials Design” (AI4Mat-ICLR-2025). This workshop will serve as a venue for researchers at the intersection of AI and materials science to address pressing scientific challenges using AI-driven techniques. AI is starting to revolutionize materials science and engineering, driving major global research initiatives from academic and government institutions and corporate research labs, alongside the rise of several startups for AI driven materials discovery. AI4Mat's holistic approach to materials design and machine learning ensures comprehensive discussions and foster novel directions across the materials landscape. AI4Mat-ICLR-2025 centers on understanding crucial and timely technical challenges that are unique to AI for materials design: 1. How Do We Build a Foundation Model for Materials Science?: The success of foundation models in various machine learning domains has led to growing relevance and interest in materials foundation models. As such, we propose a discussion that centers on understanding the complex, interdisciplinary nature of foundational models for materials and how the community can contribute towards building them. 2. What are Next-Generation Representations of Materials Data?: Materials representation learning continue to be a rapidly evolving technical challenge with unique considerations informed by real-world materials challenges.AI4Mat-ICLR-2025 also …
Workshop
[ Peridot 204-205 ]
Abstract
In the rapidly evolving landscape of AI, the development of scalable optimization methods to yield efficient and adaptive foundation models has significant demand in the space of their inference service. In specific, enabling model efficiency while allowing them to be adaptable to various new down-stream tasks has multifold challenges. Firstly, the model’s ability to quickly learn adaptive and efficient sub-model selection on different tasks requires the capability to perform continual weight updates, compute- and memory-efficient fine-tuning, and personalized adaptation. Secondly, with the increased demand for long context understanding and reasoning, the model needs to yieldsuch efficient adaptation with the informative usefulness of the query-specific token fetching. For instance, imagine a model that continually learns from current news events, adapting to the everchanging global landscape by integrating up-to-date knowledge. Such models may not only need efficient fine-tuning to new incoming data stream, but also understand efficient handling of the KV cache that may keep on growing with the requirement to handle longer contextual information. Additionally, the integration of retrieval-augmented generation (RAG) into foundation models can ensure that generated content is not only relevant, but also reflects the most current knowledge while costing the prefill size to go up. Thirdly, with such …
Workshop
[ Hall 4 #2 ]
Abstract
The financial industry is experiencing a paradigm shift propelled by rapid advancements in artificial intelligence. From algorithmic trading and fraud detection to personalized banking and investment strategies, AI technologies are redefining financial services. Our workshop aims to convene researchers, industry professionals, and policymakers to explore the latest developments, discuss challenges, and chart a course for responsible AI integration in finance.Topics of interest include, but not limit to Generative AI with applications to finance, time-series modeling, financial datasets, multi-agent systems, and practical financial applications such as forecasting, fraud detection, risk management, and quantitative finance, etc. By bringing together diverse perspectives from academia and industry, we seek to foster collaboration and drive forward advancements in the responsible use of AI in finance.
Workshop
[ Hall 4 #4 ]
Abstract
Foundation models (FMs) have become central to modern machine learning, with data playing a crucial role in their development and sparking increased attention to data-related challenges such as curation and attribution. Adapting traditional data-centric methods to FMs is challenging due to the scale of both data and model architectures, necessitating interdisciplinary collaboration and community efforts. Building on the success of the first Data Problems in Foundation Models workshop at ICLR 2024, the second workshop will address persistent and emerging data-related challenges in FM deployment. While longstanding issues in data collection, curation, and synthesis remain relevant, new challenges have arisen as FMs are integrated into a growing number of applications and become increasingly multi-modal. Concurrently, the societal impact of AI has intensified, highlighting concerns such as data copyright. These evolving challenges emphasize the need for continued, focused discussions on data-related issues in FM development. Our goals include fostering a comprehensive understanding of these challenges across the entire FM pipeline and creating a platform for interdisciplinary researchers to connect, collaborate, and drive progress. We hope this workshop will serve as a catalyst for innovative solutions to critical data challenges, shaping the future of FMs and their wide-ranging applications.
Workshop
[ Conference GHJ ]
Abstract
Both natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. Despite extensive research across machine learning, neuroscience, and cognitive science, it remains unclear what the most appropriate ways are to compare and align the representations of intelligence systems. In the second edition of the Workshop on Representational Alignment (Re^2-Align), we bring together researchers from diverse fields who study representational alignment to make concrete progress on this set of open interdisciplinary problems. We invite researchers across the machine learning, neuroscience, and cognitive science communities to participate and contribute to the workshop in two main ways: (1) via contributed papers and participation in structured discussions during the workshop; and (2) by participating in the workshop hackathon.
Workshop
[ Hall 4 #1 ]
Abstract
The goal of the I Can’t Believe It’s Not Better (ICBINB) workshop series is to promote slow science and build a community to discuss surprising and negative results, thereby encouraging a culture of transparency and shared learning. In recent years, we have witnessed a remarkable rise of Deep Learning (DL), whose impressive performance on benchmark tasks has led to increasing ambitions to deploy DL in real-world applications across all fields and disciplines. However, despite its potential, DL still faces many challenges during deployment in dynamic, real-world conditions, thus exposing practical limitations that are often overlooked in controlled benchmarks. Therefore, in this year’s ICBINB workshop, we aim to explore the challenges, unexpected outcomes, and common principles underlying similar issues and failure modes encountered across various fields and disciplines when deploying DL models in real-world scenarios. We will invite contributions and discussions from diverse fields including, but not limited to, healthcare, scientific discovery, robotics, education, equality & fairness, and social sciences. The failure modes may include suboptimal performance, concerns with the safety and reliability of applying DL models in unpredictable real-world applications, as well as ethical and societal challenges. More importantly, we aim to discuss common reasons or patterns in challenges and …
Workshop
[ Garnet 216-214 ]
Abstract
As AI systems grow more integrated into real-world applications, the traditional one-way approach to AI alignment is proving insufficient. Bidirectional Human-AI Alignment proposes a new, dynamic framework where alignment is viewed as an ongoing, reciprocal process, with both humans and AI systems adapting over time. This paradigm acknowledges the complexity of human-AI interactions and emphasizes the need for continuous adaptation to evolving human values, societal contexts, and feedback loops. Our workshop at ICLR 2025 focuses on machine learning techniques that can drive this bidirectional alignment, including reinforcement learning, interactive learning, and multi-task learning, enabling AI systems to evolve in response to real-world changes. We also explore value specification, human-in-the-loop frameworks, and scalable post-training alignment methods. Additionally, the workshop will address evaluation techniques for real-time alignment adjustments and the societal implications of maintaining alignment across diverse human populations. By fostering collaboration between AI, HCI, and social science researchers, the workshop aims to create scalable, adaptive alignment frameworks that reflect ethical and societal goals. This event offers a novel approach to alignment research, emphasizing mutual human-AI adaptation and interdisciplinary cooperation to ensure AI systems remain aligned with human values.
Workshop
[ Opal 101-102 ]
Abstract
This workshop is motivated by a fact: human beings have strong embodied intelligence in an open environment, but it is still challenging for large language models and LLM agents. Despite some progress on embodied AI in static and indoor environments, the LLM agents still struggle with tasks in large-scale outdoor environments, such as navigation, search, spatial reasoning, task planning, etc. Therefore, we propose this workshop to discuss the recent advances in the related research area and look forward to future development. Specifically, it delves into topics of outdoor embodied intelligence, such as spatial intelligence and embodied perception, reasoning and planning, decision-making and action, multi-agent and human-agent collaboration, and the development of simulators, testbeds, datasets, and benchmarks. This comprehensive exploration of embodied LLM agents in an open city environment holds the potential to advance the field of artificial intelligence and open up new applications in various domains. We also have a special poster/short paper session for those solutions that perform best in the Open Urban Environment Embodied Intelligence Competition.
Workshop
[ Hall 4 #6 ]
Abstract
As Large Language Models (LLMs) are rapidly adopted across diverse industries, concerns around their trustworthiness, safety, and ethical implications increasingly motivate academic research, industrial development, and legal innovation. LLMs are increasingly integrated into complex applications, where they must navigate challenges related to data privacy, regulatory compliance, and dynamic user interactions. These complex applications amplify the potential of LLMs to violate the trust of humans. Ensuring the trustworthiness of LLMs is paramount as they transition from standalone tools to integral components of real-world applications used by millions.This workshop addresses the unique challenges posed by the deployment of LLMs, ranging from guardrails to explainability to regulation and beyond. The proposed workshop will bring together researchers and practitioners from academia and industry to explore cutting-edge solutions for improving the trustworthiness of LLMs and LLM-driven applications. The workshop will feature invited talks, a panel discussion, interactive breakout discussion sessions, and poster presentations, fostering rich dialogue and knowledge exchange. We aim to bridge the gap between foundational research and the practical challenges of deploying LLMs in trustworthy, use-centric systems.
Workshop
[ Peridot 202-203 ]
Abstract
Probabilistic inference, particularly through the use of sampling-based methods, is a cornerstone for modeling across diverse fields, from machine learning and statistics to natural sciences such as physics, biology, and chemistry. However, many challenges exist, including scaling, which has resulted in the development of new machine learning methods. In response to these rapid developments, we propose a workshop, *Frontiers in Probabilistic Inference: learning meets Sampling* (FIP), to foster collaboration between communities working on sampling and learning-based inference. The workshop aims to center community discussions on (i) key challenges in sampling, (ii) new sampling methods, and (iii) their applications to natural sciences and uncertainty estimation. We have assembled an exciting speaker list with diverse perspectives; our goal is that attendees leave with a deeper understanding of the latest advances in sampling methods, practical insights into their applications, and new connections to collaborate on future research endeavors.
Workshop
[ Hall 4 #5 ]
Abstract
Foundation models (FMs) have revolutionized artificial intelligence (AI) research across many domains, enabling rapid adaptation to diverse downstream tasks. These FMs, trained on massive and high-quality datasets, have demonstrated remarkable performance in natural language processing (e.g., BERT [4], GPT [12], Gemini [14]), computer vision (e.g., ViT [5], VQGAN [6]), speech recognition (e.g., Whisper [13]), and multi-modal understanding (e.g., GPT-4o, LLaVA [10], QwenVL [2]). Despite these advancements, the scientific transparency and reproducibility of FMs have not kept pace. Proprietary interfaces conceal crucial details, such as training data, architectural design, and development processes, limiting scientific understanding of these models’ biases and risks. To bridge this gap, there is a growing need for truly open foundation models that the research community can access and study.In response, a surge of open science works has emerged to address the issue, encouraging transparency of FMs within the research community. Notable examples include open-access large language models (LLMs) such as Llama [15], Mistral [8], and Qwen [1], as well as extensive pre-training datasets like RedPajama [3] and The Stack [9]. These efforts have democratized access to high-performance models and sparked further innovation. Moreover, several initiatives like OLMo [7] and StarCoder [11] now offer fully transparent models, providing …
Workshop
[ Garnet 218-219 ]
Abstract
Workshop
[ Topaz 220-225 ]
Abstract
Next generation of robots should combine ideas from other fields such as computer vision, natural language processing, machine learning and many others, because the close-loop system is required to deal with complex tasks based on multimodal input in the complicated real environment. This workshop proposal focuses on generative models for robot learning, which lies in the important and fundamental field of AI and robotics. Learning-based methods in robotics have achieved high success rate and generalization ability in a wide variety of tasks such as manipulation, navigation, SLAM, scene reconstruction, proprioception, and physics modeling. However, robot learning faces several challenges including the expensive cost of data collection and weak transferability across different tasks and scenarios. Inspired by the significant progress in computer vision and natural language processing, efforts have been made to combine generative models with robot learning to address the above challenges such as synthesizing high-quality data, and incorporating generation frameworks into representation and policy learning. Besides, pre-trained large language models (LLMs), vision-language models (VLMs) and vision-language-action (VLA) models are adapted to various downstream tasks to fully leverage the rich commonsense knowledge. This progressive development enables robot learning frameworks to be applied in complex and diverse real-world tasks. This workshop …
Workshop
[ Hall 4 #3 ]
Abstract
Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the global machine learning community who wish to help tackle climate change, and is further aimed to help foster cross-pollination between researchers in machine learning and experts in complementary climate-relevant fields. Building on our past workshops on this topic, this workshop particularly aims to explore data-centric ML approaches for climate action. Data-centric ML is not only a timely topic within the ICLR community, as analyzing and engineering (pre)training datasets becomes increasingly important, but holds specific challenges and opportunities in climate-related areas. We also want to take the opportunity of ICLR being hosted in Singapore to engage with local communities and shine a light on work that deploys, analyzes or critiques …
Workshop
[ Garnet 212-213 ]
Abstract
This workshop explores the growing capabilities of large language models (LLMs), such as OpenAI's o1 model, in reasoning, planning, and decision-making, highlighting recent advances and challenges. We aim to examine how reinforcement learning methods, post-training optimization, and efficient inference techniques can further enhance LLMs' reasoning capabilities. Topics include training approach for enhancing reasoning and planning abilities, scaling inference for complex tasks, developing robust benchmarks, and extending LLMs to multi-modal and embodied environments. We will also discuss broader themes such as causal reasoning, collaborative multi-agent systems, uncertainty, and explainability to offer insights and guidance for the further development of reasoning and planning in LLMs.
Workshop
[ Peridot 201&206 ]
Abstract
Our workshop covers the widest range of topics related to World Models, including understanding, modelling, and closely aligning with cutting-edge generative AI and broader applications such as robotics and embodied AI. We are glad to announce that nine confirmed top-tier researchers including the founder of world models have confirmed to attend in person as speakers and panelists. The workshop widely targets AI researchers, industry professionals, and students interested in World Models, generative AI, reinforcement learning and related applications. Participants should have a basic understanding of generative models and reinforcement learning concepts. Familiarity with recent advancements in both fields will be beneficial but not mandatory. We also welcome submissions from researchers in the natural sciences (e.g., physics,chemistry, biology) and social sciences (e.g., pedagogy, sociology) to offer attendees a more comprehensiveperspective. In summary, our topics of interest mainly include, but are not limited to:- Understanding World Rules;- World model training and evaluation;- Scaling World Models across language, vision, and control;- World Models in general domains.For the contributed paper sessions, regarding the recent surge in publications in related areas and the success of similar workshops, we project over 250 paper submissions and over 1,500 participants.
Workshop
[ Hall 1 Apex ]
Abstract
Deep Generative Models (DGMs) have significantly advanced artificial intelligence (AI) through innovations like variational autoencoders, flow-based models, generative adversarial networks, and diffusion models. Despite their success, substantial theoretical and practical challenges remain, including the lack of rigorous theoretical frameworks, training instability, scalability issues, and challenges in adapting to structured domains. This workshop aims to bridge the gap between theory and practice by addressing two key questions: (1) How to develop comprehensive theoretical frameworks for DGMs? (2) How to develop principled strategies to improve the practical efficiency, reliability and transferability of DGMs in real-world applications? By bringing together experts from diverse backgrounds, the workshop will foster interdisciplinary collaboration to develop principled solutions, ultimately advancing the theoretical foundations and practical efficacy of DGMs.