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Mar 12, '24March 12, 2024
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Mar 13, '24March 13, 2024●(1 event)
[Seminar] Sebastian Allmeier[Seminar] Sebastian Allmeier – Who: Sebastian Allmeier When: Wednesday, March 13, 14:00-15:00 Where: IMAG 406 What: Mean-Field Approximations More: TBA Bâtiment IMAG (442) |
Mar 14, '24March 14, 2024
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Mar 15, '24March 15, 2024
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Mar 16, '24March 16, 2024
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Mar 17, '24March 17, 2024
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| MMonday | TTuesday | WWednesday | TThursday | FFriday | SSaturday | SSunday |
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26February 26, 2024
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27February 27, 2024
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28February 28, 2024
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29February 29, 2024
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March1March 1, 2024 |
2March 2, 2024
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4March 4, 2024
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5March 5, 2024
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6March 6, 2024
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7March 7, 2024
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8March 8, 2024
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9March 9, 2024
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10March 10, 2024
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11March 11, 2024
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12March 12, 2024
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[Seminar] Sebastian Allmeier – Who: Sebastian Allmeier When: Wednesday, March 13, 14:00-15:00 Where: IMAG 406 What: Mean-Field Approximations More: TBA Bâtiment IMAG (442) |
14March 14, 2024
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15March 15, 2024
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16March 16, 2024
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17March 17, 2024
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18March 18, 2024
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[Seminar] Romain Cravic – Who: Romain Cravic When: Tuesday, March 19, 14:00-15:00 Where: IMAG 406 What: Résoudre les jeux partiellement observables : Algorithme CFR et variantes de Monte-Carlo, première partie More: Dans ce séminaire en deux parties, je vous présenterai la famille des algorithmes CFR (CounterFactual Regret minimization) appliqués aux jeux sous forme extensive à information incomplète. CFR a été utilisé en 2015 par des chercheurs de l’université d’Alberta pour résoudre une version « réaliste » du poker (Heads-up limit poker). Dans la première partie nous verrons comment modéliser l’information incomplète pour les jeux à deux joueurs à somme nulle, comment définir des stratégies dans ce modèle, avant d’analyser en détail l’algorithme CFR qui calcule un approximation de l’équilibre de Nash du jeu. Pour aller plus loin, dans la seconde partie, nous étudierons les variantes dites « Monte-Carlo » de l’algorithme CFR qui sont indispensables quand on souhaite trouver des bonnes stratégies dans des jeux plus ambitieux. Bâtiment IMAG (442) |
20March 20, 2024
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21March 21, 2024
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22March 22, 2024
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23March 23, 2024
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24March 24, 2024
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25March 25, 2024
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[Seminar] Romain Cravic – Who: Romain Cravic When: Tuesday, March 26, 14:00-15:00 Where: IMAG 406 What: Résoudre les jeux partiellement observables : Algorithme CFR et variantes de Monte-Carlo, deuxième partie More: Dans ce séminaire en deux parties, je vous présenterai la famille des algorithmes CFR (CounterFactual Regret minimization) appliqués aux jeux sous forme extensive à information incomplète. CFR a été utilisé en 2015 par des chercheurs de l’université d’Alberta pour résoudre une version « réaliste » du poker (Heads-up limit poker). Dans la première partie nous verrons comment modéliser l’information incomplète pour les jeux à deux joueurs à somme nulle, comment définir des stratégies dans ce modèle, avant d’analyser en détail l’algorithme CFR qui calcule un approximation de l’équilibre de Nash du jeu. Pour aller plus loin, dans la seconde partie, nous étudierons les variantes dites « Monte-Carlo » de l’algorithme CFR qui sont indispensables quand on souhaite trouver des bonnes stratégies dans des jeux plus ambitieux. Bâtiment IMAG (442) |
27March 27, 2024
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28March 28, 2024
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29March 29, 2024
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30March 30, 2024
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31March 31, 2024
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April1April 1, 2024 |
2April 2, 2024
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[Seminar] Victor Boone – Who: 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, 2024
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5April 5, 2024
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6April 6, 2024
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7April 7, 2024
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8April 8, 2024
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9April 9, 2024
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10April 10, 2024
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[Seminar] Charles Arnal – Who: 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, 2024
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13April 13, 2024
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14April 14, 2024
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15April 15, 2024
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16April 16, 2024
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17April 17, 2024
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18April 18, 2024
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19April 19, 2024
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20April 20, 2024
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21April 21, 2024
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22April 22, 2024
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23April 23, 2024
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24April 24, 2024
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25April 25, 2024
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26April 26, 2024
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27April 27, 2024
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28April 28, 2024
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29April 29, 2024
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Seminar Rémi Castera – Correlation of Rankings in Matching Markets Bâtiment IMAG (442) |
May1May 1, 2024 |
2May 2, 2024
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3May 3, 2024
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4May 4, 2024
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5May 5, 2024
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