ALT 2021

ALT 2021
32nd International Conference on Algorithmic Learning Theory
Ordinal	32
Event in series	ALT
Dates	2021/03/16 (iCal) - 2021/03/19
Homepage:	http://algorithmiclearningtheory.org/alt2021/#
Location
Location:	Paris, Online
Loading map... {"minzoom":false,"maxzoom":false,"mappingservice":"leaflet","width":"280px","height":"280px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"wmsoverlay":false,"copycoords":false,"static":false,"zoom":false,"defzoom":14,"layers":["OpenStreetMap"],"overlaylayers":[],"resizable":false,"enablefullscreen":false,"scrollwheelzoom":true,"markercluster":false,"clustermaxzoom":20,"clusterzoomonclick":true,"clustermaxradius":80,"clusterspiderfy":true,"geojson":"","locations":[{"text":"\u003Cb\u003E\u003Cdiv class=\"mw-parser-output\"\u003E\u003C/div\u003E\u003C/b\u003E\u003Chr /\u003E\u003Cdiv class=\"mw-parser-output\"\u003E\u003C/div\u003E","title":"","link":"","lat":48.8588897,"lon":2.3200410217201,"icon":""}],"imageoverlays":null}
Table of Contents

The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning.

Topics

* 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 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.
*