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