Events in August–September 2017
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July 31, 2017
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AugustAugust 1, 2017 |
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August 14, 2017
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August 15, 2017
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August 29, 2017
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August 31, 2017
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SeptemberSeptember 1, 2017 |
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September 7, 2017(1 event)
keynote LIGkeynote LIG – |
September 8, 2017
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September 11, 2017
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September 12, 2017
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September 14, 2017(1 event)
Computing with coins (by Jean-Marc Vincent)Computing with coins (by Jean-Marc Vincent) – The Head and tail random process appears, since the beginning of the art of computation, as a fundamental part of computer science. This talk will explore at a very basic level several ideas about computation of numbers, evaluation of quantities, checking techniques or recommendation evaluation. By small examples, we’ll try to establish links through ages between the probabilistic and algorithmic thinking. Bâtiment IMAG (442) |
September 15, 2017
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September 20, 2017
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September 21, 2017(1 event)
Kleinberg 's Grid Unchained (by Fabien Mathieu, Nokia)Kleinberg 's Grid Unchained (by Fabien Mathieu, Nokia) – One of the key features of small-worlds is the ability to route messages with few hops only using local knowledge of the topology. In 2000, Kleinberg proposed a model based on an augmented grid that asymptotically exhibits such property. In this paper, we propose to revisit the original model from a simulation-based perspective. Our approach is fueled by a new algorithm that can draw an augmenting link in Õ(1). The resulting speed gain enables detailed numerical evaluations. We show for example that in practice, the augmented scheme proposed by Kleinberg is more robust than predicted by the asymptotic behavior, even for very large finite grids. We also propose tighter bounds on the performance of Kleinberg's routing algorithm. At last, we show that, fed with realistic parameters, the model gives results in line with real-life experiments. Bâtiment IMAG (442) |
September 22, 2017
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September 27, 2017
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September 28, 2017(1 event)
On-line speed scaling minimizing expected energy consumption for real-time tasks, by Stephan Plassart (Polaris)On-line speed scaling minimizing expected energy consumption for real-time tasks, by Stephan Plassart (Polaris) – We present a Markov Decision Process (MDP) approach to compute the optimal on-line speed scaling policy to minimize the energy consumption of a processor executing a finite or infinite set of jobs with real-time constraints. The policy is computed off-line but used on-line. We provide several qualitative properties of the optimal policy: monotonicity with respect to the jobs parameters, comparison with on-line deterministic algorithms. Numerical experiments show that our proposition performs well when compared with off-line optimal solutions and out-performs on-line solutions oblivious to statistical information on the jobs. Bâtiment IMAG (442) |
September 29, 2017
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September 30, 2017
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OctoberOctober 1, 2017 |
- April 3, 2024 @ Bâtiment IMAG (442) -- [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.
- April 11, 2024 @ Bâtiment IMAG (442) -- [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.
- April 30, 2024 @ Bâtiment IMAG (442) -- Seminar Rémi Castera
Correlation of Rankings in Matching Markets