chchenhui/mlrbench-tasks huggingface dataset:
# Foundations of RL and Control: Connections and New Perspectives

## About

Despite rapid advances in machine learning, solving large-scale stochastic dynamic programming problems remains a significant challenge. The combination of neural networks with RL has opened new avenues for algorithm design, but the lack of theoretical guarantees of these approaches hinders their applicability to high-stake problems traditionally addressed using control theory, such as online supply chain optimization, industrial automation, and adaptive transportation systems. This workshop focuses on recent advances in developing a learning theory of decision (control) systems, that builds on techniques and concepts from two communities that have had limited interactions despite their shared target: reinforcement learning and control theory.

This workshop aims to reinforce the connection between reinforcement learning and control theory by bringing together researchers from both fields. In particular, we invite contributions on all fundamental and theoretical aspects, with a special emphasis on topics that connect both fields and provide new perspectives. Contributions that bridge theory and applications are also welcome. We believe that significant progress in tackling large-scale applications can only be achieved through collaborative efforts and a mutual understanding of each field’s strengths and approaches. Our workshop is dedicated to fostering dialogue and collaboration, paving the way for breakthroughs in complex dynamic programming challenges and interactive systems.

## Topics 
We invite researchers to submit papers on the topics listed below.  Technical topics include, but are not limited to, the following aspects:

- Performance measures and guarantees: Stability, robustness, regret bounds, sample-complexity, stochastic vs non-stochastic approaches, MDPs etc.
- Fundamental assumptions: Linear and non-linear systems, excitation, stability, etc.
- Fundamental limits: Results that mathematically characterize the difficulty of a given problem, statistical, information theoretic and computational lower bounds.
- Computational aspects: Efficient algorithms, computational hardness, approximations, etc.
- Topology: Continuous-state and action spaces vs discrete spaces; Discrete and continuous time analysis.
- Models: Bandits, Markov Decision Processes, Linear and nonlinear control, partial observability, POMDPs, partial monitoring, etc
- Data Acquisition & Exploration: Exploration-exploitation trade-offs, pure-exploration, experimental design.
- Offline vs. online: Open-loop and closed loop control, offline and online reinforcement learning and hybrid approaches.
- Planning and learned search: Dynamic programming, tree search and planning algorithms.
- Target applications: Formalization of applications such as autonomous vehicles, robots, industrial processes, recommender systems, internet routing, hardware optimization, hyper-parameter optimization and AutoML …
- Benchmarks: Evaluation of algorithms and theoretical results on a suitable collection of problems.
# Workshop on Efficient and Accessible Foundation Models for Biological Discovery

## About

There is a growing gap between machine learning (ML) research on biology-inspired problems and the actual broad-based use of ML in the lab or the clinic. This gap is especially pressing in the context of foundation models and other large ML models. Accessibility and efficiency concerns limit the adoption of these models by biologists and clinicians. Large ML models may require extensive GPU clusters to train, while most biological labs only have access to much more modest computational resources. The usability of these models for non-expert users is also a concern, as is the need to iteratively adapt these models based on lab discoveries.

This workshop seeks to bring ML and biomedical researchers together to identify interdisciplinary approaches to design and apply large, complex ML models for biomedical discovery. We invite researchers from academia and industry to submit original papers to bridge the accessibility and efficiency gap between ML research and wet lab use. All accepted papers will be invited to present posters at the workshop, and a few will be invited to give individual spotlight presentations.

## Topics

We are seeking original submissions in topics including, but not limited to:

- Parameter-, memory-, and compute-efficient foundation models for biological data, including model compression and quantization techniques
- Algorithms for training efficient generative models in biology
- Efficient fine-tuning and adaptation of biological foundation models
- Accessible cloud/web-based methods for foundational biological discovery
- Knowledge distillation and transfer learning across biological contexts
- Lab in the loop: iterative approaches to refine ML models based on initial experimental results
- Hypothesis-driven machine learning in biology and uncertainty modeling in biological foundation models
# AI for Math

## Workshop Summary

Mathematical reasoning is one of the most advanced forms of human intelligence. Humans develop formal languages for rigorously describing mathematical problems and deriving mathematical knowledge. The machine learning community has endeavored to develop neural models with mathematical reasoning capabilities as humans. On the other hand, a shared vision in the community is that the models collaborate with humans for mathematical discoveries. 

The goal of this workshop is to bring together researchers working on various domains to discuss the progress and the future of applying AI technologies to mathematics. As mathematics is fundamental for almost all modern sciences (including computer science), a vast range of related topics are also within our scope. To this end, this workshop focuses on several crucial yet underexplored problems. Specifically, we are expecting attendants from various backgrounds, institutions, and disciplines to discuss areas related to the following: 
- **Autoformalization and the reversed auto-informalization**: How can we develop methods that improve the precision of the autoformalization process from natural language proof to formal proof, and as a dual process describing a formal proof in natural language?

- **Automated theorem proving**: How do build consistent theorem proving? How do we relieve or solve the intermediate step errors in proving? 

- **Automated theorem generation**: Can neural models generate new and practically valid theorems? How do we take full advantage of such generated new theorems? 

- **Code augmentation and auxiliary for mathematical reasoning**: How can the handy and plentiful code data facilitate the models to conduct mathematical reasoning? 

- **Formal verification and code generation**: How can progress made in AI for Math help or be directly deployed to the field of formal verification? What are the common technical difficulties? How can AI systems be able to write provably correct code, given any (formal) specifications? 

In addition to the problem areas above, we also welcome research work related to the following topics:
- **Measurement**: How do we measure autoformalization?
- **Reasoning in related areas**: program synthesis, software verification, neurosymbolic reasoning, logical reasoning.
- **Applications**: Applying mathematical reasoning techniques to sciences, finance, education, etc.
# AI for Science: Scaling in AI for Scientific Discovery

## About

Dramatic developments in AI have led to its increasing adoption in science as a means to model complex phenomena, generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this series of workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and various scientific communities. Throughout history, bridging seemly different fields has brought overarching benefits, with notable examples: entropy in thermodynamics and information theory, neuroscience and AI, and algorithms inspired by discoveries in science (e.g. genetic algorithm, simulated annealing and diffusion-based generative models). In the current era, successes of AI methods in different fields of science have alluded to the general effectiveness of common themes: large simulated datasets, enforcing problem symmetries, and foundation model architectures. Our mission is to bring more scientists to attend ICML to share different perspectives on the use of AI, and to illuminate exciting research directions for AI researchers. 

## Interested Areas

We welcome submissions from all AI for Science areas, but we concentrate some of our talks and panels on scaling in AI for Science.

- How scaling can help AI for Science?

- How scaling can be done in AI for Science?

- How scaling change the Pareto frontier of methodology, interpretability and discovery?

- What is the limitation of scaling and what is the cure for it?
# Aligning Reinforcement Learning Experimentalists and Theorists

## Overview

Recent progress in reinforcement learning (RL) has powered breakthroughs in various real-world problems, gathering considerable attention and investment. However, it has also exposed a significant gap between theoretical and experimental developments.
RL theory has grown significantly in the past two decades. Research has characterized the inherent difficulty of various settings, and has designed a wide variety of algorithms to reach optimal performances. Furthermore, a huge leap has been made in understanding how to handle large state spaces using function approximation techniques, identifying key structural properties that enable efficient learning.

However, despite theoretical guarantees, applying RL algorithms to complex problems faces challenges. Theoretical algorithms often focus on simplified settings, making them hard to apply to real-world complexities. Furthermore, optimizing for worst-case scenarios, which include unlikely situations, can lead to algorithms that perform poorly on practical tasks. Yet, while specialized algorithms offer empirical success, they might not translate to other problems due to their specific design, and the reliance on heuristics and engineering fixes further widens the gap between theory and practice.

With this workshop, we aim to bring theorists and experimentalists together to drive future research in RL.

## Desiderata
While theorists and experimentalists share a common interest in advancing the field, their research objectives, methodologies, and challenges sometimes diverge significantly. This workshop aims to bridge this gap and to shed light on recent developments and synergies in both communities. Specifically, we aim to promote the following long-term desiderata.
- Communicate existing results. As the field evolves rapidly, theorists and experimentalists often find themselves immersed in their domain, occasionally overlooking valuable insights and challenges encountered by the other. Participants will have the opportunity to present key findings, best practices, and lessons learned, emphasizing the importance of cross-disciplinary awareness. This proactive sharing of knowledge will help create a collaborative atmosphere that promotes a deeper appreciation for the existing works and encourages fruitful discussions on the current state and future directions in RL.
- Identify new problem classes of practical interest. We aim to emphasize new structures and perspectives that have not been widely investigated yet. Experimentalists can present algorithms that work surprisingly well but lack theoretical understanding. Equally important are the cases where algorithms fail despite expectations. This collaboration will ensure that theoretical progress addresses the most compelling issues faced in practice and that advancements in empirical research will get the attention of theorists, creating a mutually beneficial exchange of ideas.
# Automated Reinforcement Learning: Exploring Meta-Learning, AutoML, and LLMs

## About

The past few years has seen a surge of interest in reinforcement learning, , with breakthrough successes of applying RL in games, robotics, chemistry, logistics, nuclear fusion and more. These headlines, however, blur the picture of what remains a brittle technology, with many successes relying on heavily engineered solutions. Indeed, several recent works have demonstrated that RL algorithms are brittle to seemingly mundane design choices . Thus, it is often a significant challenge to effectively apply RL in practice, especially on novel problems, limiting its potential impact and narrowing its accessibility.

In this workshop, we want to bring together different communities working on solving these problems. A variety of distinct sub-communities spanning RL, Meta-Learning and AutoML have been working on making RL work out-of-the-box in arbitrary settings - this is the AutoRL setting. Recently, with the emergence of LLMs and their in-context learning abilities, they have significantly impacted all these communities. There are LLM agents tackling traditional RL tasks as well as few-shot RL agents increasing efficiency and generalization that are also trying to automate RL. LLMs have also been influencing AutoML directly with papers such as OptFormer . However, there is currently little crossover between these communities. As such, we want to create the space to connect them and cross-pollinate ideas automating RL. We believe closer connections between these communities will ultimately lead to faster and more focused progress on AutoRL and an in-person workshop is the ideal way to allow for greater interaction between them. Through a mixture of diverse expert talks and opportunity for conversation, we hope to emphasize the many facets of current AutoRL approaches and where collaboration across fields is possible.

## Focused Area

The workshop will focus on novel and unpublished work including, but not limited to, the areas of:

- LLMs for reinforcement learning
- In-context reinforcement learning
- Meta-reinforcement learning
- RL algorithm discovery
- Fairness & interpretability via AutoRL
- Curricula and open-endedness in RL
- AutoML for reinforcement learning
- Reinforcement learning for LLMs
- NAS for deep reinforcement learning
- Theoretical guarantees for AutoRL
- Feature- & Hyperparameter importance for RL algorithms
- Demos of AutoRL systems
- Hyperparameter agnostic RL algorithms
# Workshop on Data-Centric Machine Learning Research


## Scope

Large-scale foundation models are revolutionizing machine learning, particularly in vision and language domains. While model architecture received significant attention in the past, recent focus has shifted towards the importance of data quality, size, and diversity, and provenance.

This workshop aims to highlight cutting-edge advancements in data-centric approaches for large-scale foundation models in new domains, in addition to language and vision, and engage the vibrant interdisciplinary community of researchers, practitioners, and engineers who tackle practical data challenges related to foundation models. By featuring innovative research and facilitating collaboration, it aims to bridge the gap between dataset-centric methodologies and the development of robust, versatile foundation models that are able to work in and across a variety of domains in service of humanity.

## Topic

We solicit papers covering the following topic:
- Data sources for large-scale datasets:
- Construction of datasets from large quantities of unlabeled/uncurated data
- Model-assisted dataset construction
- Quality signals for large-scale datasets
- Datasets for evaluation
- Datasets for specific applications.
- Impact of dataset drifts in large-scale models
- Ethical considerations for and governance of large-scale datasets
- Data curation and HCI
- Benchmarks such as DataPerf, DynaBench, and DataComp
# Workshop on Foundation Models in the Wild

## Abstract

In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, 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 in their in-the-wild/real-world deployments. The Workshop on FMs in the wild delves into the urgent need for these 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 critical decision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeply about this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of adaptivity, reliability and efficiency. Some of the fundamental questions that this workshop aims to address are:

1. Real-world Adaptation: In practical applications, how can we leverage the comprehensive knowledge in FMs to adapt them for specific domains, such as drug discovery, education, or clinical health?
2. Reliability and Responsibility: How can foundation models work reliably outside their training distribution? And how can we address issues like hallucination and privacy?
3. Safety, Ethics, and Fairness in Society: How do we ensure that the deployment of FMs preserving safety, ethics, and fairness within society, safeguarding against biases and unethical use?
4. Practical Limitations in Deployment: How can FMs tackle challenges in practical applications, such as system constraints, computational costs, data acquisition barriers, response time demands?
# Workshop on Geometry-grounded Representation Learning and Generative Modeling

## Motivation

By recognizing that nearly all data is rooted in our physical world, and thus inherently grounded in geometry and physics, it becomes evident that representation learning should preserve this grounding in order to remain meaningful. For example, preserving group transformation laws and symmetries through equivariant layers is crucial in domains such as computational physics, chemistry, robotics, and medical imaging, and leads to effective and generalizable architectures and improved data efficiency. Similarly, in generative models applied to non-Euclidean data spaces, maintaining the manifold structure is essential in order to obtain meaningful samples. Therefore, this workshop focuses on the principle of grounding in geometry.

## Topics

We solicit submissions that present theoretical research, methodologies, applications, insightful analysis, and even open problems, within the following topics (list not exhaustive):

- Structure-preserving learning
    - Preservation of symmetries; E.g., through equivariant operators.
    - Dynamical systems on manifolds; Representation learning and generative modeling using ordinary, stochastic, and differential equations (ODEs, SDEs, PDEs) on manifolds.
    - Computing with geometric representations; Such as the processing of multi-vectors using geometric algebra, steerable vectors using Clebsch-Gordan products, and hyperbolic features using Fréchet means.

- Structure-inducing learning
    - Self-supervised learning; E.g., learning to embed data in geometric latent spaces through (geodesic) distance-based similarity metrics.
    - Geometric priors; E.g., soft constraints on model weights.
    - Physics-Informed Neural Networks; E.g., inducing the structure of established physical and geometric laws into neural networks through dedicated losses.

- Generative modeling and density estimation
    - Geometric latent variable models; I.e., the use of latent variables that live in a manifold.
    - New Methods; And adaptations of methods capable of:
    - Generating geometric objects; E.g., generating atomic point clouds or shapes.
    - Generating fields over manifolds; E.g., generating vector fields or spherical signals.

- Grounding in theory
    - Theoretical frameworks; Unifying analyses and formulations that provide a generalizing perspective on deep learning paradigms.
    - Open problems; Identifying and addressing unresolved questions and challenges that lie at the intersection of geometry and learning
# High-dimensional Learning Dynamics 2024: The Emergence of Structure and Reasoning

## About

The unprecedented scale and complexity of modern neural networks have revealed emergent patterns in learning dynamics and scaling behaviors. Recent advances in analyzing high-dimensional systems have uncovered fundamental relationships between model size, data requirements, and computational resources while highlighting the intricate nature of optimization landscapes. This understanding has led to deeper insights into the architecture design, regularization, and the principles governing neural learning at scale.  

## Areas

The HiLD workshop seeks to spur research and collaboration around: 

* Developing analyzable models and dynamics to explain observed deep neural network phenomena;

* Competition and dependencies among structures and heuristics, e.g., simplicity bias or learning staircase functions; 

* Creating mathematical frameworks for scaling limits of neural network dynamics as width and depth grow;

* Provably explaining the role of the optimization algorithm, hyper-parameter choices, and neural network architectural choices on training/test dynamics;

* Relating optimizer design and loss landscape geometry to implicit regularization, inductive bias, and generalization;

* High-dimensionality, where intuitions from low-dimensional geometry tend to lead to inaccurate (and often misleading) properties of the machine learning models on large real-world datasets;

* Connecting model architectures and data distributions to generalization, memorization, and forgetting.