Polaris-tt: Till Kletti (Introducing the expohedron for fair and useful rankings)
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December 9, 2021
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News
- Journée au vert POLARIS 2022/05/23
- DATAMOVE/POLARIS picnic 2021/06/22
- DATAMOVE/POLARIS BBQ 2019 2019/06/14
- POLARIS Bootcamp (May 2019) 2019/05/24
- slides of Andras Gyorgy 2016/01/15
Next seminars
- 2:00 pm – 3:00 pm, April 30, 2024 – Seminar Rémi Castera
Events
Events in April–May 2024
MMonday TTuesday WWednesday TThursday FFriday SSaturday SSunday April
1April 1, 20242April 2, 2024[Seminar] Victor Boone
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April 3, 2024Who: Victor Boone
When: Wednesday, April 3, 14:00-15:00
Where: 447
What: Learning MDPs with Extended Bellman Operators
More: Efficiently learning Markov Decision Processes (MDPs) is difficult. When facing an unknown environment, where is the adequate limit between repeating actions that have shown their efficiency in the past (exploitation of your knowledge) and testing alternatives that may actually be better than what you currently believe (exploration of the environment)? To bypass this dilemma, a well-known solution is the "optimism-in-face-of-uncertainty" principle: Think of the score of an action as being the largest that is statistically plausible.
The exploration-exploitation dilemma then becomes the problem of tuning optimism. In this talk, I will explain how optimism in MDPs can be all rephrased using a single operator, embedding all the uncertainty in your environment within a single MDP. This is a story about "extended Bellman operators" and "extended MDPs", and about how one can achieve minimax optimal regret using this machinery.
Bâtiment IMAG (442)4April 4, 20245April 5, 20246April 6, 20247April 7, 20248April 8, 20249April 9, 202410April 10, 2024[Seminar] Charles Arnal
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April 11, 2024Who: Charles Arnal
When: Thursday, April 11, 14:00-15:00
Where: 442
What: Mode Estimation with Partial Feedback
More: The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments. These new learning pipelines call for new theoretical frameworks. In this paper, we formalize core aspects of weakly supervised and active learning with a simple problem: the estimation of the mode of a distribution using partial feedback. We show how entropy coding allows for optimal information acquisition from partial feedback, develop coarse sufficient statistics for mode identification, and adapt bandit algorithms to our new setting. Finally, we combine those contributions into a statistically and computationally efficient solution to our problem.
Bâtiment IMAG (442)12April 12, 202413April 13, 202414April 14, 202415April 15, 202416April 16, 202417April 17, 202418April 18, 202419April 19, 202420April 20, 202421April 21, 202422April 22, 202423April 23, 202424April 24, 202425April 25, 202426April 26, 202427April 27, 202428April 28, 202429April 29, 2024Seminar Rémi Castera
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April 30, 2024Correlation of Rankings in Matching Markets
Bâtiment IMAG (442)May
1May 1, 20242May 2, 20243May 3, 20244May 4, 20245May 5, 20246May 6, 20247May 7, 20248May 8, 20249May 9, 202410May 10, 202411May 11, 202412May 12, 202413May 13, 202414May 14, 202415May 15, 202416May 16, 202417May 17, 202418May 18, 202419May 19, 202420May 20, 202421May 21, 202422May 22, 202423May 23, 202424May 24, 202425May 25, 202426May 26, 202427May 27, 202428May 28, 202429May 29, 202430May 30, 202431May 31, 2024June
1June 1, 20242June 2, 2024Meta
