2021 IEEE East Asian School of Information Theory
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Tuesday, August 3rd
8:30am: Opening (slides)
8:50am: (Tutorial A1) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 1, TN1)
9:50am: Break
10:00am: (Tutorial A2) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 2, TN2)
10:50am: Break
11:00am: (Tutorial A3) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 3, TN3)
12:00pm: Lunch on your own
1:30pm: (Tutorial B1) Prof. Vincent Tan, NUS: Common information and non-interactive correlation distillation (slides)
2:20pm: Break
2:30pm: (Tutorial B2) Prof. Vincent Tan, NUS: Common information and non-interactive correlation distillation (slides)
3:20pm: Break
3:30pm: (Tutorial B3) Prof. Vincent Tan, NUS: Common information and non-interactive correlation distillation (slides)
4:30pm: Adjourn
Wednesday, August 4th
8:30am: (Tutorial A4) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 4, TN4)
9:20am: Break
9:30am: (Tutorial A5) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 5, TN5)
10:20pm: Break
10:30pm: (Tutorial A6) Prof. Changho Suh, KAIST: Fair machine learning (Lecture 6, TN6)
11:30am: Lunch on your own
1:00pm: (Tutorial C1) Prof. Lingfei Jin, Fudan University: A brief introduction to (maximal recoverable) locally repairable codes (Lecture 1)
1:50pm: Break
2:00pm: (Tutorial C2) Prof. Lingfei Jin, Fudan University: A brief introduction to (maximal recoverable) locally repairable codes (Lecture 2)
2:50pm: Break
3:00pm: (Tutorial C3) Prof. Lingfei Jin, Fudan University: A brief introduction to (maximal recoverable) locally repairable codes (Lecture 3)
4:00pm: Adjourn
Thursday, August 5th
8:30am: (Tutorial D1) Prof. Lalitha Sankar, ASU: Bridging information theory and machine learning: A loss function perspective (Lecture 1)
9:20am: Break
9:30am: (Tutorial D2) Prof. Lalitha Sankar, ASU: Bridging information theory and machine learning: A loss function perspective (Lecture 2)
10:20am: Break
10:30am: (Tutorial D3) Prof. Lalitha Sankar, ASU: Bridging information theory and machine learning: A loss function perspective (Lecture 3)
11:30am: Lunch on your own
3:30pm: (Tutorial E1) Prof. Deniz Gunduz, ICL: Semantic and goal-oriented Communications: Information theoretic foundations and applications to machine learning (slides)
4:20pm: Break
4:30pm: (Tutorial E2) Prof. Deniz Gunduz, ICL: Semantic and goal-oriented Communications: Information theoretic foundations and applications to machine learning (slides)
5:20pm: Break
5:30pm: (Tutorial E3) Prof. Deniz Gunduz, ICL: Semantic and goal-oriented Communications: Information theoretic foundations and applications to machine learning (slides)
6:30pm: Adjourn
Friday, August 6th
8:30am: (Goldsmith Lecture 1), Prof. Yuejie Chi, CMU: Non-asymptotic statistical and computational guarantees of reinforcement learning algorithms (slides)
9:20am: Break
9:30am: (Goldsmith Lecture 2), Prof. Yuejie Chi, CMU: Non-asymptotic statistical and computational guarantees of reinforcement learning algorithms (slides)
10:20am: Break
10:30am: (Goldsmith Lecture 3), Prof. Yuejie Chi, CMU: Non-asymptotic statistical and computational guarantees of reinforcement learning algorithms (slides)
11:30am: Lunch on your own
1:00pm: Poster Session
3:00pm: Closing