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iclr2023_bands
# Backdoor Attacks and Defenses in Machine Learning ## Overview Backdoor attacks aim to cause consistent misclassification of any input by adding a specific pattern called a trigger. Unlike adversarial attacks requiring generating perturbations on the fly to induce misclassification for one single input, backdoor att...
1
iclr2023_dg
# What do we need for successful domain generalization? ## Workshop Description The real challenge for any machine learning system is to be reliable and robust in any situation, even if it is different compared to training conditions. Existing general purpose approaches to domain generalization (DG) — a problem setti...
2
iclr2023_ml4materials
# Machine Learning for Materials ## Overview Many of the world's most crucial challenges, such as access to renewable energy, energy storage, or clean water, are currently fundamentally bottlenecked by materials challenges. The discovery of new materials drives the development of key technologies like solar cells, b...
3
iclr2023_mlgh
## Machine Learning & Global Health During the Covid-19 pandemic, in spite of the impressive advances in machine learning in recent decades, the successes of this field were modest at best. Much work remains, for both machine learning and global health researchers, to deliver true progress in global health. This works...
4
iclr2023_mrl
# Multimodal Representation Learning: Perks and Pitfalls ## About the workshop Following deep learning, multimodal machine learning has made steady progress, becoming ubiquitous in many domains. Learning representations from multiple modalities can be beneficial since different perceptual modalities can inform each o...
5
iclr2023_nf
## Neural Fields across Fields: Methods and Applications of Implicit Neural Representations Addressing problems in different science and engineering disciplines often requires solving optimization problems, including via machine learning from large training data. One class of methods has recently gained significant at...
6
iclr2023_physics4ml
## Physics for Machine Learning Combining physics with machine learning is a rapidly growing field of research. Thus far, most of the work in this area focuses on leveraging recent advances in classical machine learning to solve problems that arise in the physical sciences. In this workshop, we wish to focus on a slig...
7
iclr2023_rrl
## Reincarnating RL This inaugural workshop at ICLR 2023 (in-person) aims to bring further attention to the emerging paradigm of reusing prior computation in RL, which we refer to as reincarnating RL. Specifically, we plan to discuss potential benefits of reincarnating RL, its current limitations and associated challe...
8
iclr2023_rtml
## Trustworthy and Reliable Large-Scale Machine Learning Models In recent years, the landscape of AI has been significantly altered by the advances in large-scale pre-trained models. Scaling up the models with more data and parameters has significantly improved performance and achieved great success in a variety of ap...
9
iclr2023_snn
## Overview of Sparsity in Neural Networks Deep networks with billions of parameters trained on large datasets have achieved unprecedented success in various applications, ranging from medical diagnostics to urban planning and autonomous driving, to name a few. However, training large models is contingent on exception...
10
iclr2023_sr4ad
## Overview of Scene Representations for Autonomous Driving This workshop aims to promote the real-world impact of ML research toward self-driving technology. While ML-based components of modular stacks have been a huge success, there remains progress to be made in the development of integration strategies and interme...
11
iclr2023_tml4h
## Trustworthy Machine Learning for Healthcare Workshop Machine learning (ML) has achieved or even exceeded human performance in many healthcare tasks, owing to the fast development of ML techniques and the growing scale of medical data. However, ML techniques are still far from being widely applied in practice. Real-...
12
iclr2023_trustml
## Pitfalls of limited data and computation for Trustworthy ML Due to the impressive performance of ML algorithms, they are increasingly used in a wide range of applications that impact our daily lives. These include sensitive domains like healthcare, banking, social services, autonomous transportation, social media, ...
13
iclr2023_tsrl4h
## Workshop on Time Series Representation Learning for Health Time series data have been used in many applications in healthcare, such as the diagnosis of a disease, prediction of disease progression, clustering of patient groups, online monitoring, and dynamic treatment regimes, to name a few. More and more methods b...
14
iclr2024_agi
# How Far Are We From AGI ## Topics This workshop aims to become a melting pot for ideas, discussions, and debates regarding our proximity to AGI. We invite submissions on a range of topics including, but not limited to: 1. **Frontiers of AGI research:** Examples include AI agents, embodied AI, retrieval-based and t...
15
iclr2024_al4de
# AI4DifferentialEquations In Science ## Background Over the past decade, the integration of Artificial Intelligence (AI) for scientific exploration has grown as a transformative force, propelling research into new realms of discovery. The AI4DifferentialEquations in Science workshop at ICLR 2024 invites participants...
16
iclr2024_bgpt
## Bridging the Gap Between Practice and Theory in Deep Learning The success of deep learning practices has driven the rapid development of learning theory. However, recent studies have pointed out that contrasting scenarios and conclusions exist between many existing theories and their corresponding real-world applic...
17
iclr2024_dmlr
## Data-centric Machine Learning Research 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 prove...
18
iclr2024_dpfm
# Navigating and Addressing Data Problems for Foundation Models ## Overview Foundation Models (FMs, e.g., GPT-3/4, LLaMA, DALL-E, Stable Diffusion, etc.) have demonstrated unprecedented performance across a wide range of downstream tasks. Following the rapid evolution, as researchers strive to keep up with the unders...
19
iclr2024_gem
# Generative and Experimental Perspectives for Biomolecular Design ## About 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 signifi...
20
iclr2024_genai4dm
## Generative Models for Decision Making Generative Artificial Intelligence (AI) has made significant advancements in recent years, particularly with the development of large language and diffusion models. These generative models have demonstrated impressive capabilities across various domains, such as text, image, au...
21
iclr2024_globalai
# Global AI Cultures ## Description Building globally-inclusive artificial intelligence systems that encodes and respects cultural sensibilities as well as performs well for users across cultural contexts, is an important goal as we deploy AI products globally. However existing AI evaluation, design and deployment pr...
22
iclr2024_llm4agents
# Large Language Model (LLM) Agents ## About This Workshop delves into the significance of agents driven by large language models (LLMs), a topic that has recently sparked intense discussions. Building on the current huge progress on LLMs, we'll focus on autonomous agents that perform intricate tasks in both real an...
23
iclr2024_mefomo
## Workshop on Mathematical and Empirical Understanding of Foundation Models Foundation models (FMs) have revolutionized machine learning research across domains. These models are trained on extensive, highly varied datasets and can be quickly adapted to solve many tasks of interest. FMs are extremely effective on lan...
24
iclr2024_mlgenx
# Machine Learning for Genomics Explorations ## Overview 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 clin...
25
iclr2024_pml
# Privacy Regulation and Protection in Machine Learning ## Introduction Recent advances in artificial intelligence greatly benefit from data-driven machine learning methods that train deep neural networks with large scale data. The usage of data should be responsible, transparent, and comply with privacy regulations....
26
iclr2024_pml4lrs
# Practical ML for Limited/Low Resource Settings ## Introduction The constant progress being made in machine learning needs to extend across borders if we are to democratize ML in developing countries. Adapting state-of-the-art (SOTA) methods to resource constrained environments such as developing countries can be c...
27
iclr2024_r2fm
# Reliable and Responsible Foundation Models ## Overview 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, offer...
28
iclr2024_realign
# Workshop on Representational Alignment ## About 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 ...
29
iclr2024_setllm
# Workshop on Secure and Trustworthy Large Language Models ## About The striding advances of large language models (LLMs) are revolutionizing many long-standing natural language processing tasks ranging from machine translation to question-answering and dialog systems. However, as LLMs are often built upon massive am...
30
iclr2024_ts4h
# Learning from Time Series for Health Time series data are ubiquitous in healthcare, from medical time series to wearable data, and present an exciting opportunity for machine learning methods to extract actionable insights about human health. However, huge gap remain between the existing time series literature and w...
31
iclr2025_agenticai
# Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation ## About the 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 f...
32
iclr2025_ai4chl
# AI for Children: Healthcare, Psychology, Education ## About the Workshop 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 t...
33
iclr2025_ai4mat
## About the Workshop The AI for Accelerated Materials Discovery (AI4Mat) Workshop NeurIPS 2024 provides an inclusive and collaborative platform where AI researchers and material scientists converge to tackle the cutting-edge challenges in AI-driven materials discovery and development. Our goal is to foster a vibrant e...
34
iclr2025_ai4na
# Workshop on AI for Nucleic Acids AI4NA aims to popularize AI applications for nucleic acids and introduce nucleic acid research challenges to the broader AI community. This workshop aims to spotlight nucleic acids as the next frontier for AI research. By bringing together experts from machine learning and biology, w...
35
iclr2025_bi_align
# 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 ...
36
iclr2025_buildingtrust
# 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 increas...
37
iclr2025_data_problems
# Workshop on Navigating and Addressing Data Problems for Foundation Models 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 da...
38
iclr2025_delta
# 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 prac...
39
iclr2025_dl4c
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 f...
40
iclr2025_embodiedai
# Workshop on Embodied Intelligence with Large Language Models In Open City Environment 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. Depsite some progresses on embodied AI on static and...
41
iclr2025_financial_ai
# 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...
42
iclr2025_fm_wild
# Workshop on Foundation Models in the Wild 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 s...
43
iclr2025_fpi
# 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...
44
iclr2025_gem
# 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 signi...
45
iclr2025_haic
HAIC 2025, the First Workshop on Human-AI Coevolution, focuses on the emerging field of Human-AI Coevolution (HAIC) to understand the feedback loops that emerge through continuous human-AI coadaptation. This workshop focuses on new approaches beyond AI performance benchmarks, exploring multiple levels of analysis span...
46
iclr2025_icbinb
# I Can't Believe It's Not Better: Challenges in Applied Deep Learning Why don’t deep learning approaches always deliver as expected in the real world? Dive deep into the pitfalls and challenges of applied deep learning. In recent years, we have witnessed a remarkable rise of deep learning (DL), whose impressive per...
47
iclr2025_llm_reason_and_plan
# Workshop on Reasoning and Planning for Large Language Models ## About The Workshop 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 ...
48
iclr2025_lmrl
# Learning Meaningful Representations of Life (LMRL) ## About this workshop Since the last time that the LMRL workshop was held at NeurIPS 2022, interest in representation learning for biology has surged, with new ideas challenging traditional approaches and sparking discussions on how best to capture the complexity ...
49
iclr2025_mcdc
# Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning ## Summary While the success of large-scale deep learning models has hinged on the ``bigger is better'' approach – scaling model size and training data – this paradigm may rapidly be reaching an inflection point. Beyond the prohib...
50
iclr2025_mldpr
# 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-valui...
51
iclr2025_mlgenx
# 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 clini...
52
iclr2025_mlmp
# Workshop on Machine Learning Multiscale Processes Given low-level theory and computationally-expensive simulation code, how can we model complex systems on a useful time scale? Fundamental laws of Nature, Standard Model of Physics, and the most applied part of it, quantum mechanics, are well established. Theoretica...
53
iclr2025_nfam
# New Frontiers in Associative Memories ## About This Workshop Associative Memory (AM) is a core notion in psychology responsible for our ability to link people's names to their faces and to remember the smell of a strawberry when we see one. Mathematical formalizations of AM date back to the 1960s-1980s [...] . For i...
54
iclr2025_question
# 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 perva...
55
iclr2025_re_align
# 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 an...
56
iclr2025_sci_fm
# Workshop on Open Science for Foundation Models ## About the Workshop Foundation models (FMs) have transformed AI research but lack scientific transparency. The SCI-FM workshop aims to address this by fostering open science, reproducibility, and the sharing of open-source models and datasets. We invite contributions ...
57
iclr2025_scope
# Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models ## About This Workshop 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 specifi...
58
iclr2025_scsl
## Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions Reliance on spurious correlations due to simplicity bias is a well-known pitfall of deep learning models. This issue stems from the statistical nature of deep learning algorithms and their inductive biases at all stages, including dat...
59
iclr2025_sllm
## Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference 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 ...
60
iclr2025_ssi_fm
# Scaling Self-Improving Foundation Models without Human Supervision ## Overview The availability of internet data, while vast, is ultimately finite or at least growing at a pace that lags behind the consumption needs of foundation models (FMs) during pre-training. Perhaps as is most evident with large language model...
End of preview. Expand in Data Studio

This repository contains the benchmark dataset of MLR-Bench. We collect 201 tasks from ICLR/NeurIPS/ICML workshops over the past three years. The followings record the metadata of our collection.

Workshop without an official website or deleted

  • icml2024_fminwild
  • neurips2024_attrib_late
  • neurips2024_gsai
  • neurips2024_rlfm
  • iclr2023_ai4abm
  • iclr2023_ml4iot
  • iclr2023_mldd
  • iclr2023_NeSy_GeMs
  • neurips2023_new_in_ml
  • neurips2023_ai4mat
  • icml2023_esfomo

Non-general workshops

  • neurips2024_queerinai
  • iclr2024_africanlp
  • iclr2023_africanlp
  • iclr2023_IndabaX_Rwanda
  • icml2023_lxai

Repeated workshops

  • iclr2023_dl4c
  • iclr2023_mefomo
  • iclr2023_re_align and iclr2024_realign (TODO: check the details and delete one)
  • icml2024_fminwild potentially repeat with other workshops

Missing workshop for unknown reason

Workshop information to be updated

Workshop Links

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