[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2021

AISTATS 2021 Program Schedule

All times are in Pacific Daylight Time (PDT). For more details please see the live schedule, which is posted on the virtual site.

Schedule for Day 1: Tue, April 13

Day 1: Tue, April 13
09:00-10:15am Invited Talk (Emmanuel Candes)
10:15-10:30am Break
10:30-11:30am Oral Session 1:Theory of Statistical and Deep Learning Methods
11:30am-12:30pm Oral Session 2: Sampling Methods
12:30-02:00pm WiML and Caucus for Women in Statistics Affinity Event, and Mentorship sessions
02:00-04:00pm Poster Session 1 (91 papers)
04:00-04:15pm Break
04:15-05:15pm Oral Session 3: Bandits, Reinforcement Learning / Optimization
05:15-06:15pm Oral Session 4: Theory and Practice of Machine Learning
06:15-06:30pm Break
06:30-08:30pm Poster Session 2 (91 papers)
08:30-10:00pm Mentorship sessions


Schedule for Day 2: Wed, April 14

Day 2: Wed, April 14
04:00-06:00am Black in AI Affinity Event, and Mentorship sessions
06:00-8:00am Poster Session 3 (91 papers)
08:00-08:15am Break
08:15-9:15am Oral Session 5: Theory and Methods of Learning
9:15-10:15am Oral Session 6: Bandits, Reinforcement Learning / Learning Theory / Sparse Methods
10:15-10:30am Break
10:30-11:30am Oral Session 7: Optimization / Learning Theory / Generalization
11:30am-12:30pm Oral Session 8: Graphs and Networks
12:30-12:45pm Break
12:45-2:45pm Poster Session 4 (91 papers)
02:45-03:00pm Break
03:00-04:00pm Mentorship sessions
04:00-05:15pm Invited Talk (Bin Yu)
05:15-05:30pm Break
05:15-06:15pm Caucus for Women in Statistics Affinity Event
05:30-07:00pm Mentorship sessions


Schedule for Day 3: Thu, April 15

Day 3: Thu, April 15
04:00-06:00am Mentorship sessions
06:00-07:15am Invited Talk (Kyle Cranmer)
07:15-07:30am Break
07:30-9:30am Poster Session 5 (91 papers)
09:30-09:45am Break
09:45-11:45am Mentorship sessions
11:45am-12:00pm Break
12:00-01:00pm Oral Session 9: Fairness / Privacy / Decision Making / Data Cleaning
01:00-02:00pm Oral Session 10: Generalization / Reinforcement Learning / Optimization
02:00-02:15pm Break
02:15-03:15pm Oral Session 11: Deep Learning / High-dimensionality
03:15-04:15pm Oral Session 12: Learning Theory
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