July 10, 2025
Colocated with ACM EC 25, Stanford University, Stanford, CA
https://sites.google.com/view/ole-2025/home-page

Submission deadline: June 6, 2025

The workshop focuses on the intersection of online learning and economics, exploring how learning algorithms are increasingly used for decision-making in strategic economic settings such as markets and platforms. The event brings together researchers from diverse fields to discuss current challenges, share recent advances, and foster collaboration. The scope of the workshop extends beyond online learning to encompass other relevant domains of machine learning, including, for example, learning theory and reinforcement learning.

Call for posters: More info on the workshop website

June 30, 2025
Lyon, France at COLT 2025
https://vaidehi8913.github.io/predictions-and-uncertainty-colt25/

Submission deadline: May 25, 2025

Predictions from machine learning systems are increasingly being used as inputs to downstream algorithms and decision-making tasks. However, these predictions can be unreliable, and often suffer from biases or overconfidence, highlighting the need for rigorous approaches to modeling their uncertainty. The goal of the workshop is to bring together researchers from across different communities to learn about exciting developments and explore connections between these emerging lines of research. Topics of interest include Conformal Prediction, Algorithms with Predictions, Robust Statistics, Risk-Averse Decision Making, and other approaches for estimating and propagating uncertainty in statistical and algorithmic applications. The workshop includes a poster session, and we invite submissions from a broad range of areas related to uncertainty quantification and using unreliable predictions.

June 30, 2025
Lyon, France
https://tasc-workshop.github.io

Submission deadline: May 23, 2025
Registration deadline: May 23, 2025

The Theory of AI for Scientific Computing (TASC) workshop will be held as part of COLT 2025 and aims to advance the theoretical understanding of AI-augmented scientific computing, including capabilities and limitations of recent methods, principled architectures, unique data-generation and training considerations when connecting ML to downstream tasks, the sample complexity potential of transfer learning and active sampling, and ensuring the robustness of deployed systems and the validity of new discoveries. Our goal is to foster new theory that can bridge the gap between rapid methodological developments and their ultimate goals: scientific understanding and deployable computational systems. Join us on June 30, 2025 in Lyon, France for an exciting program of keynotes, posters, awards, and discussions about building principled connections between learning, algorithms, and the physical sciences, identifying promising scientific computing objectives for AI, and formalizing theoretical problems that will inspire continued progress.

July 30, 2025
Lyon, France (colocated with COLT 2025)
https://fopt-workshop.github.io/

Submission deadline: May 19, 2025

This COLT 2025 workshop seeks to explore the theoretical and practical aspects of the post-training of LLMs, across a diversity of domains and abstractions. The workshop aims to bring together experts from diverse fields including theoretical reinforcement learning, optimization, statistical learning theory, as well as more empirical directions, to identify critical research opportunities.

As a part of FoPt 2025, we invite abstract submissions to a single non-archival track. See our call for abstracts for more details. The submission deadline is Monday, May 19th, 2025.

June 30 – July 4, 2025
Lyon, France
https://learningtheory.org/colt2025/wtc.html

Submission deadline: April 18, 2025

The 38th Annual Conference on Learning Theory (COLT 2025) will dedicate a day during the main conference program (June 30 – July 4) exclusively to contributed and invited (1) workshops, (2) tutorials, and (3) community-building events (e.g., affinity workshops, mentoring activities, socials). We invite proposals for these in-person events: please submit your proposal submit by email by April 18th AoE (see the call for more details).

April 14-16, 2025
Online
https://waldo-workshop.github.io/2025.html

On behalf of the organizers – Guy Blanc, Quanquan Liu, Shivam Nadimpalli, Samson Zhou – we would like to invite you to the Workshop on Algorithms for Large Data (Online) 2025, which will be from Monday, April 14th to Wednesday, April 16th. We are pleased to host a fantastic set of speakers giving research talks and due to generous funding from SIGACT, registration is free and all are welcome! Please register by April 7, 2025 for access to the virtual platform. In addition, we will also feature poster sessions on Monday and Wednesday. For more details, please visit https://waldo-workshop.github.io/2025.html. We hope you will join us!

March 17-21, 2025
University of Warwick
https://warwick.ac.uk/fac/sci/maths/research/events/2024-2025/algebraiccomplexity/

Algebraic Complexity Theory is a vibrant field that has been seeing a tremendous amount of activity in the recent years. Its classical questions have been interwoven with deep questions from algebraic geometry, invariant theory, and representation theory, with recent exciting connections to metacomplexity and proof complexity. The workshop brings together experts from these fields to discuss the current state of the art, discover new connections, and set the directions for the future. Each plenary speaker will explain a recent result and break down its main ideas to a level that is accessible for the mathematically diverse audience. Remote participation via MS Teams is possible.

July 21-23, 2025
New Brunswick
http://dmac.rutgers.edu/events/details?eID=3156

This workshop titled, “Hardness of Approximation in P” is part of a larger DIMACS Special Focus on Fine-Grained Complexity. The workshop will feature multiple tutorials and is primarily aimed at researchers and graduate students in complexity theory. Limited funding is also available for students. Registration is free but required.

March 31 – April 4, 2025
Ruhr University Bochum, Germany
https://qi.rub.de/wact

The 8th edition of the Workshop on Algebraic Complexity Theory will be held on March 31-April 4, 2025 at Ruhr University Bochum, Germany. This workshop will bring together experts and junior researchers in the vibrant field of algebraic complexity theory and related topics. Attendance is free of charge, but registration is required. Please see the website for more information.

August 2, 2025
Vanderbilt University in Nashville, Tennessee
https://fsmlims.wixsite.com/fsml25

Submission deadline: March 2, 2025

The Frontiers in Statistical Machine Learning (FSML) Workshop is a one-day event designed to bring together researchers at the forefront of statistical machine learning. Taking place on August 2nd, 2025, at Vanderbilt University in Nashville, Tennessee, this workshop is conveniently scheduled adjacent to JSM 2025 and is proudly sponsored by IMS. FSML aims to foster collaboration and exchange of ideas on cutting-edge topics in machine learning, including the science of deep learning and statistical learning from heterogeneous data sources and generalization. With a dynamic format that features peer-reviewed poster presentations, and insights from leading ML experts, this workshop offers an excellent platform for advancing statistical practices through innovative ML algorithms.