ICML 2023 Workshop

PAC-Bayes Meets Interactive Learning

Hawaii Convention Center, Honolulu, Hawaii
Meeting Room 314
28th July, 2023


Interactive learning encompasses online learning, continual learning, active learning, bandits, reinforcement learning, and other settings where an algorithm must learn while interacting with a continual stream of data. Such problems often involve exploration-exploitation dilemmas, which can be elegantly handled with probabilistic and Bayesian methods. Deep interactive learning methods leveraging neural networks are typically used when the setting involves rich observations, such as images. As a result, both probabilistic and deep interactive learning methods are growing in popularity. However, acquiring observations in an interactive fashion with the environment can be costly. There is therefore great interest in understanding when sample-efficient learning with probabilistic and deep interactive learning can be expected or guaranteed. Within statistical learning theory, PAC-Bayesian theory is designed for the analysis of probabilistic learning methods. It has recently been shown to be well-suited for the analysis of deep learning methods. This workshop aims to bring together researchers from the broad Bayesian and interactive learning communities in order to foster the emergence of new ideas that could contribute to both theoretical and empirical advancement of PAC-Bayesian theory in interactive learning settings.

Call For Papers

Important dates:
  • Submission deadline: May 31, 2023
  • Reviewer registration link: https://docs.google.com/forms/d/1B4iX6dl1QgxPfRUmhqrriAWNllN-rFlRPR9WAjUwQNw/edit
  • Submission link: https://openreview.net/group?id=ICML.cc/2023/Workshop/PAC-Bayes_Meets_Interactive_Learning
  • Notification of acceptance: June 13th, 2023
  • Posters and camera ready: July 21st, 2023
All accepted papers will have a poster presentation, and we will select a few papers for contributed talks of approximately 15 minutes each. Authors of accepted papers will be encouraged to submit a short video presentation of their poster (e.g., voice over slides/poster). We will make these poster presentation videos publicly available at the start of the conference. Please note that at least one author of each accepted paper must be available for the poster presentation.

Topics: We are interested in (but not limited to) the following topics:
  • Explaining the success of existing interactive learning algorithms with PAC-Bayesian theory
  • PAC-Bayesian analysis of exploration-exploitation trade-offs
  • PAC-Bayes bounds under distribution shift
  • PAC-Bayes bounds under adversarial corruptions
  • Development of practically useful interactive learning algorithms using PAC-Bayesian theory.
Submission instructions: The page limit is 4 pages (exluding references). Submissions may include supplementary material, but reviewers are only required to read the first 4 pages. Submissions should use the template in this LaTeX style file. The reviewing process is double-blind, so the submissions should be anonymized and should not contain information that could identify the authors. Parallel submission (to a journal, conference, workshop, or preprint repository) is allowed.

Speakers and Panelists

Karolina Dziugaite

Senior Research Scientist, Google Brain
Adjunct Professor, McGill University

Pascal Germain

Assistant Professor, Université Laval
Canada CIFAR AI Chair

Anirudha Majumdar

Assistant Professor
Princeton University, USA

Aaditya Ramdas

Assistant Professor
Carnegie Mellon University, Amazon

Jonas Rothfuss

PhD Student
ETH Zürich, Switzerland

Other speakers will be announced soon.

Workshop Schedule

Time Event
08:50 Welcome
09:00 PAC-Bayes Tutorial - Pascal Germain
10:00 Posters + break 1
11:00 Invited talk 1 - TBA
11:30 Invited talk 2 - TBA
12:00 Lunch break
13:30 Invited talk 3 - TBA
14:00 Invited talk 4 - TBA
14:30 Posters + break 2
15:30 Contributed talks
16:30 Panel discussion
17:30 Posters + social + end

Accepted Papers



Audrey Durand

Canada CIFAR AI Chair
Assistant Professor, Université Laval

Hamish Flynn

PhD Student, TU Darmstadt, Bosch Center for Artificial Intelligence

Benjamin Guedj

Associate Professor, University College London
Tenured Research Scientist, Inria
Turing Fellow, The Alan Turing Institute

Maxime Heuillet

PhD Student, Université Laval, Thales Group

Melih Kandemir

Associate Professor, University of Southern Denmark


Funding from CIFAR is available to support Canadian students who might not otherwise be able to attend the event, with a focus on students who identify as members of underrepresented groups.


Please contact the organisers at: pacbayes.interactivelearning@gmail.com