ALT: Algorithmic Learning Theory

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2025 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
ALT 2024 35th Annual Conference on Algorithmic Learning Theory
Feb 25, 2024 - Feb 28, 2024 San Diego, California, USA Sep 26, 2023
ALT 2021 Algorithmic Learning Theory
Mar 16, 2021 - Mar 19, 2021 Paris, France Sep 30, 2020
ALT 2019 Algorithmic Learning Theory
Mar 22, 2019 - Mar 24, 2019 Chicago, USA Sep 28, 2018
ALT 2018 International Conference on Algorithmic Learning Theory
Apr 7, 2018 - Apr 9, 2018 Lanzarote, Spain Oct 27, 2017
ALT 2016 International Conference on Algorithmic Learning Theory
Oct 19, 2016 - Oct 21, 2016 Bari, Italy May 13, 2016
ALT 2013 International Conference on Algorithmic Learning Theory
Oct 6, 2013 - Oct 9, 2013 Singapore, Republic of Singapore May 11, 2013
ALT 2011 Algorithmic Learning Theory
Oct 5, 2011 - Oct 7, 2011 Espoo, Finland May 18, 2011
ALT 2010 The 21st International Conference on Algorithmic Learning Theory
Oct 6, 2010 - Oct 8, 2010 Canberra, Australia May 12, 2010
 
 

Present CFP : 2024

The Algorithmic Learning Theory (ALT) 2024 conference will be held in San Diego, CA on February 25-28th 2024. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to:

Design and analysis of learning algorithms.
Statistical and computational learning theory.
Online learning algorithms and theory.
Optimization methods for learning.
Unsupervised, semi-supervised, and active learning.
Interactive learning, planning and control, and reinforcement learning.
Privacy-preserving data analysis.
Learning with additional societal and strategic considerations: e.g., fairness, economics.
Robustness of learning algorithms to adversarial agents.
Artificial neural networks, including deep learning.
High-dimensional and non-parametric statistics.
Adaptive data analysis and selective inference.
Learning with algebraic or combinatorial structure.
Bayesian methods in learning.
Learning in distributed and streaming settings.
Game theory and learning.
Learning from complex data: e.g., networks, time series.
Theoretical analysis of probabilistic graphical models.

While the primary focus of the conference is theoretical, authors are welcome to support their analysis by including relevant experimental results.

Accepted papers will be published electronically in the Proceedings of Machine Learning Research (PMLR), and will be presented at the conference as a full-length talk. Authors of accepted papers will have the option of opting out of the proceedings in favor of a 1-page extended abstract, which will point to an open access archival version of the full paper reviewed for ALT.


Important dates

Paper submission deadline: September 26, 2023, Anywhere On Earth
Author feedback: Nov 11-17, 2023
Author notification: Mid-December, 2023


Conference format

The conference will be in-person and will not be hybrid. At least one author of each accepted paper will be required to present their paper in-person at the conference. When travel is not possible, we encourage authors to find alternative presenters in the community attending the conference.


Dual submission policy

Conferences: In general, submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings may not be submitted to ALT.

Journals: Submissions that are substantially similar to papers that are already published in a journal at the time of submission may not be submitted to ALT.
Rebuttal phase

This year there will be a rebuttal phase during the review process. Authors will have an opportunity to provide a short response to the initial reviews.
 

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