chchenhui/mlrbench-tasks huggingface dataset:
# Workshop on Bidirectional Human-AI Alignment

This workshop focuses on bidirectional Human AI alignment, a paradigm shift in how we approach the challenge of human-AI alignment, which emphasizes the dynamic, complex, and evolving alignment process between humans and AI systems. This is grounded on the "bidirectional human-AI alignment" framework (see Definition and ReadingList) derived from a systematic survey of over 400 interdisciplinary alignment papers in Machine Learning (ML), Human Computer Interaction (HCI), Natural Language Processing (NLP), more domains. Particularly, it involves two directions to maximize its benefits for human society.

- Aligning AI with Humans (AI-centered perspective): focuses on integrating human specifications into training, steering, customizing, and monitoring AI systems;
- Aligning Humans with AI (Human-centered perspective): aims to preserve human agency and empower humans to critically evaluate, explain, and collaborate with AI systems.

## Challenges & Goals
The rapid advancements in general-purpose AI has precipitated the urgent need to align these systems with values, ethical principles, and goals that match the context of use, i.e., for individuals using an AI system, and for the holistic society at large. Traditionally, AI alignment has been viewed as a static, one-way process, with a primary focus on shaping AI systems to achieve desired outcomes and prevent negative side effect. However, as AI systems are taking on more complex decision-making roles, this **unidirectional AI alignment is inadequate to capture the dynamic, complicated, and evolving interactions between humans and AI systems**.

The core objectives of this workshop are twofold: (1) broadening the current understanding of AI alignment and inviting more researchers to collectively explore the bidirectional human-AI alignment studies; (2) fostering interdisciplinary collaboration between researchers in multi-disciplinary domains, such as AI, HCI, and social sciences, creating a platform for exchange and innovation.

## Scopes & Topics
This workshop aims to explore the design space of bidirectional human-AI alignment from a comprehensive view, calling for submissions from various disciplines and topics, including but not limited to (see all in Call For Papers):

- Scope: Broader Definitions and clarifications of Current Alignment Research;
- Opinions: Position Papers and Roadmaps for Future Alignment Research;
- Specification: Representation approaches of Human Values, Behavior, Cognition, Societal Norms for AI Alignment;
- Methods: Reinforcement Learning with Human Feedback, Algorithms, Interaction Mechanisms, UX Design for Alignment;
- Evaluation: Benchmarks, Metrics or Human Evaluation for Multi-objective AI Alignment;
- Deployment: Customizable Alignment, Steerability, Interpretability, and Scalable Oversight;
- Societal Impact and Policy: Fostering an Inclusive Human-AI Alignment Ecosystem.
# Workshop on Building Trust in Language Models and Applications

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 Scope
This workshop has a broad focus, including but not limited to:

1. Metrics, benchmarks, and evaluation of trustworthy LLMs

2. Improving reliability and truthfulness of LLMs

3. Explainability and interpretability of language model responses

4. Robustness of LLMs

5. Unlearning for LLMs

6. Fairness of LLMs

7. Guardrails and regulations for LLMs

8. Error detection and correction
# Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy

We are excited to invite submissions to the ICLR 2025 Workshop on Deep Generative Models: Theory, Principle, and Efficacy. This workshop aims to explore challenges and opportunities in advancing the theoretical foundations and practical applications of deep generative models (DGMs).

Theory topics include, but are not limited to:
- Expressivity of deep generative models: investigate the expressivity of deep generative models and their performance variations across different datasets
- Optimization and generalization of deep generative models
- Solving stochastic processes for deep generative models
- Sampling methods
- Model Stability and Convergence Analysis in DGMs
- Implicit Bias and Regularization in Generative Models
- Robustness and Generalization Boundaries of Generative Models
- Latent Space Geometry and Manifold Learning

Application areas include, but are not limited to:
- Improved sampling schemes
- Adversarial Robustness and Defense Mechanisms
- Scalability and Efficiency in High-Dimensional Generative Modeling
- Multimodal Generative Modeling Algorithms
- Structured Data Modeling
- Generative models for scientific discovery (AI4Science)
The thrid DL4C workshop titled "Emergent Possibilities and Challenges in Deep Learning for Code" provides a vibrant platform for researchers to share their work on deep learning for code, emphasizing emergent possibilities and challenges, for example: agentic methods for programming tasks, post-training and alignment for code, developer productivity and HCI for code, open science and responsible AI for code, and benchmarking and evaluation for code.

We invite original research paper submissions from any topic that is relevant to deep learning for code. This year, we specifically welcome submissions addressing recent challenges like:

- Agentic Methods for Programming Tasks Agents able to solve realistic coding tasks, such as solving GitHub issues or software developing tasks.

- Post-training and Alignment for Code Alignment for code, including but not limited to how to learn from human feedback, execution feedback, and AI feedback for better code generation.

- Developer Productivity and HCI for Code Adaptation of models to users’ needs to increas developer productivity, including studies on human-AI interaction for code from different disciplines (Machine Learning, Human-Computer Interaction, and Software Engineering, etc.).

- Open Science and Responsible AI for Code Contributions from researchers who follow responsible AI practices and strive for openness and transparency in their work and who are willing to share their code, models, and data. We also welcome contributions from researchers interested in developing open science practices for deep learning for code.

- Benchmarking and Evaluation for Code Benchmarks for code such execution-based benchmarks, code understanding, code efficiency, model-based judges, and project-level context.

Other topics of interest include but are not limited to, for example:

- Reinforcement Learning for Code
- Data for Code
- Pre-training Methods and Representation for Code
- Natural Language To Code
- Formal Methods for Code
- Program Repair
- Code Translation
- Code Explanation
- Code Summarization
- Code Generation for Applications Beyond Code such as Reasoning, Decision Making, and Algorithmic Discovery
# Workshop on Advances in Financial AI: Opportunities, Innovations and Responsible AI

The financial industry is undergoing a transformative shift fueled by rapid advancements in artificial intelligence. From algorithmic trading and fraud detection to personalized banking and investment strategies, AI is redefining how financial services operate. 

This workshop will bring together researchers, industry professionals, and policymakers to share the latest developments, address emerging challenges, and establish a roadmap for responsible AI integration in finance.

## Topics of Interest:

Topics of interest include, but are not limited to, Generative AI with applications in finance, time-series modelling, financial datasets, multi-agent systems, and practical financial applications such as forecasting, fraud detection, risk management, and quantitative finance.
# Frontiers in Probabilistic Inference: Learning meets Sampling

## About the Workshop
The Frontiers in Probabilistic Inference: Sampling meets Learning (FPI) workshop at ICLR 2025 focuses on modern approaches to probabilistic inference to address the challenging and under-explored area of sampling from an unnormalized distribution. Sampling spans a wide range of difficult and timely problems from molecular dynamics simulation, and Bayesian posterior inference/inverse problems to sampling from generative models weighted by target density (e.g. finetuning, inference-time alignment). We hope to provide an inclusive and collaborative environment to discuss emerging ML methods for learning samplers and their applications to real-world problems. We aim to facilitate discussions around identifying some key challenges of learning-based approaches, compared to classical sampling approaches, along with techniques to overcome them.

We will center workshop discussions around the following topics/questions:

- Sampling methods and their connections to optimal transport and optimal control.

- Classical sampling approaches and how learning accelerates them.

- Connections between sampling methods and physics.

- Understanding sampling from theoretical perspectives. 

- Applications of sampling to natural sciences, Bayesian inference, LLM fine-tuning, and more.


We invite all submissions of original work across three different tracks:

- Research Papers

- Challenges and Reflections

- Benchmarks and Datasets


### Research Papers

Goals: The goal of the Research Papers track is to highlight all original research work in the field of sampling. Some examples of the research topics include, but aren't limited to:

- Bayesian posterior inference/inverse problem.

- Amortized sampling from Botlzmann densities.

- Sampling from generative models (diffusion model and LLMs) weighted by target density: i.e. fine-tuning, inference-time alignment, etc.

- Applications: e.g. molecular dynamics simulations, statistical physics, etc.

### Challenges and Reflections

Goals: The goal of the Challenges and Reflections track is to explore setbacks, unexpected outcomes, and the valuable lessons learned from methods that didn’t achieve their intended goals. Some examples of the research topics include, but aren't limited to:

- Ideas and methods that didn't make a paper but discussing the methodology and the results can provide valuable insights for future researchers. 

- Challenges and open problems in the field. We encourage the researchers to discuss 1. Why the current state-of-the-art research fails to address those challenges and 2. What are some of the directions that the researchers believe the community must focus on and pursue to overcome those challenges. 

### Benchmarks and Datasets

Goals: The goal of the Benchmarks and Datasets track is to encourage submissions of papers which highlight a dataset, tools or benchmarks that can be disseminated to the community during the workshop.
# Workshop on Generative and Experimental Perspectives for Biomolecular Design

Biomolecular design, through artificial engineering of proteins, molecules, 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 real-world applications. 

The Generative and Experimental perspectives in bioMolecular design (GEM) workshop seeks to bridge this gap by bringing computationalists and experimentalists together. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and pinpoint biological problems ready for ML. 

GEM is collaborating with Nature Biotechnology to allow exceptional submissions to be considered for fast-tracking in their journal. GEM features two tracks of submission: an in-silico generative machine learning track, and an experimental track for any papers that have wet lab results. 

Our lineup features renowned scientists as panelists and emerging leaders as speakers, 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.

GEM has two tracks: a machine learning track, and a biology track. 

These topics include but are not limited to the following:

### ML track
- Generative ML advancements for biomolecular design with in silico results.

    - Inverse design of all biomolecules

    - Modelling biomolecular data

    - Model interpretability

### Biology track

- Biological problems and data ripe for generative ML and/or employment of ML for biomolecular design with wet lab experimental results. 

    - Biological problems apt for ML applications

    - High-throughput data generation methods

    - Adaptive experimental design

    - Benchmarks, datasets, and oracles
# The Future of Machine Learning Data Practices and Repositories

## About this workshop

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.

This workshop aims to facilitate a broad conversation about the impact of ML datasets on research, practice, and education—working to identify current issues, propose new techniques, and establish best practices throughout the ML dataset lifecycle. In particular, we highlight the role of data repositories in ML—administrators of these repositories, including OpenML, HuggingFace Datasets, and the UCI ML Repository, will contribute their perspective on how ML datasets are created, documented, and used and discuss the practical challenges of implementing and enforcing best practices on their platforms. By involving representatives from three major ML repositories and influential researchers from ML, law, governance, and the social sciences, our intent is that this workshop can serve as a catalyst for real positive changes to the ML data ecosystem.

We invite submissions related to the role of data practices in machine learning, including but not limited to the following topics of interest:

- Data repository design and challenges, particularly those specific to ML
- Dataset publication and citation
- FAIR and AI-ready datasets
- Licensing for ML datasets
- ML dataset search and discovery
- Comprehensive data documentation
- Data documentation methods for foundation models
- Data curation and quality assurance
- Best practices for revising and deprecating datasets
- Dataset usability
- Dataset reproducibility
- FAIR ML models
- Benchmark reproducibility
- Holistic and contextualized benchmarking
- Benchmarking and leaderboard ranking techniques
- Overfitting and overuse of benchmark datasets
- Non-traditional/alternative benchmarking paradigms
# Workshop on Machine Learning for Genomics Explorations

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.

This year, the workshop will feature three distinct tracks designed to welcome a diverse array of researchers in the field of machine learning and biology: the Main Track including application and ML topics, the Special Track on LLMs and Agentic AI, and the Tiny Papers Track. Papers in the main and the special tracks must be prepared and submitted as a single file: 8 pages for the paper, with unlimited pages for references, the impact statement, and appendices.

Both contributions introducing new ML methods to existing problems and those that highlighting and explaining open problems are welcome. We also encourage submissions related to application of molecular biology, including but not limited to, single-cell RNA analysis, bulk RNA studies, proteomics, and microscopy imaging of cells and/or tissues.

We consider a broad range of subject areas including but not limited to the following topics.

Main Track:

- Foundation models for genomics
- Biological sequence design
- Interpretability and Generalizability in genomics
- Causal representation learning
- Perturbation biology
- Modeling long-range dependencies in sequences, single-cell and spatial omics
- Integrating multimodal perturbation readouts
- Active learning in genomics
- Generative models in Biology
- Multimodal representation learning
- Uncertainty quantification
- Optimal transport
- Experimental design for Biology
- Graph neural network and knowledge graph
- New datasets and benchmarks for genomics explorations

Special Track on LLMs and Agentic AI:

- Pre-training multi-omics models
- Synthetic data generation and data quality for pre-training, fine-tuning and instruction tuning
- Fine-tuning (SFT, RLHF, RL with lab feedback, ...) on novel tasks
- In-context learning with large-context models
- Reasoning through prompt engineering or architectural design
- Interpretability and uncertainty quantification
- Knowledge retrieval (RAG, knowledge graph, ...)
- Efficient interactive system designs (agents, humans, and biological tools)
- Training/fine-tuning LLM-powered design and planning engine
# Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI

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, allowing users to assess when to trust the outputs and when human oversight may be needed.

This workshop seeks to address the gap by defining, evaluating, and understanding the implications of uncertainty quantification for autoregressive models and large-scale foundation models. Researchers from machine learning, statistics, cognitive science, and human-computer interaction are invited to contribute through submitted papers, and structured discussions on key questions and topics:

- How can we create scalable and computationally efficient methods for estimating uncertainty in large language models?
- What are the theoretical foundations for understanding uncertainty in generative models?
- How can we effectively detect and mitigate hallucinations in generative models while preserving their creative capabilities?
- How is uncertainty affecting multimodal systems?
- What are the best practices for communicating model uncertainty to various stakeholders, from technical experts to end users?
- What practical and realistic benchmarks and datasets can be established to evaluate uncertainty for foundation models?
- How can uncertainty estimates guide decision-making under risk ensuring safer and more reliable deployment?
# Representational Alignment

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 intelligent systems (Sucholutsky et al., 2023). In the second edition of the Workshop on Representational Alignment (Re-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 in the workshop, and to contribute to the workshop in two ways:

First, in the form of contributed papers that address questions of representational alignment that stem from the following central theme: When and why do intelligence systems learn aligned representations, and how can scientists and engineers intervene on this alignment? Other questions topical for this year’s workshop include:

- To what extent does representational alignment indicate shared computational strategies among biological and artificial systems?
- How have current alignment metrics advanced our understanding of computation, and what measurement approaches should we explore next?
- How can we develop more robust and generalizable measures of alignment that work across different domains and types of representations?
- How can we systematically increase (or decrease) representational alignment among biological and artificial systems?
W- hat are the implications (positive and negative) of increasing or decreasing representational alignment between systems, on behavioral alignment, value alignment, and beyond?

Second, by participating in our workshop hackathon. Since the first iteration of Re-Align workshop, there have been numerous debates around the metrics that we use to measure representational similarity, which is often taken as a measure of representational alignment (e.g., Cloos et al., 2024; Khosla et al., 2024; Lampinen et al., 2024; Schaeffer et al., 2024). As of now, there is little consensus on which metric best achieves the goal of identifying similarity between systems. The hackathon component of the workshop will be helpful in articulating the consequences of these methodologies by facilitating a common language among researchers, and as a result increase the reproducibility of research in this subdomain.