ALT 2020

ALT 2020
31st International Conference on Algorithmic Learning Theory
Ordinal	31
Event in series	ALT
Dates	2020/02/08 (iCal) - 2020/02/11
Homepage:	http://alt2020.algorithmiclearningtheory.org/
Location
Location:	San Diego, USA
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":32.7174202,"lon":-117.162772,"icon":""}],"imageoverlays":null}
Important dates
Papers:	2019/09/20
Submissions:	2019/09/20
Notification:	2019/11/24
Papers:	Submitted 128 / Accepted 38 (29.7 %)
Committees
PC chairs:	Aryeh Kontorovich, Gergely Neu
PC members:	Yasin Abbasi-Yadkori, Pierre Alquier, Shai Ben-David, Nicolò Cesa-Bianchi, Andrew Cotter, Ilias Diakonikolas
Table of Contents

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.
• Connections of learning with other mathematical fields.
• Artificial neural networks, including deep learning.
• High-dimensional and non-parametric statistics.
• Learning with algebraic or combinatorial structure.
• Bayesian methods in learning.
• Learning with system constraints: e.g. privacy, memory or communication budget.
• Learning from complex data: e.g., networks, time series.
• Interactions with statistical physics.
• Learning in other settings: e.g. social, economic, and game-theoretic.