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