Events in January–February 2024
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[Talk] Felipe Garrido-Lucero
[Talk] Felipe Garrido-Lucero
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January 10, 2024Who: Felipe Garrido-Lucero
When: Wednesday 10/01, 14:00-15:00
Where: Room 106 and zoom (https://univ-grenoble-alpes-fr.zoom.us/my/mertikop)
What: Fairness Challenges in AI: Insights into Data-set Valuation and Matching Markets
More: Fairness is an increasingly research subject within various computer science sub-disciplines such as machine learning and market design. In this presentation we will discuss two possible fairness challenges: the data-set valuation problem and the price of fairness in bipartite matching markets.
The data-set valuation problem addresses measuring the contributions of agents when collaborating on machine learning tasks. By employing tools from both machine learning and game theory, we model this as a cooperative game and present an approach to approximate the Shapley value of players. The method demonstrates superior performance compared to Monte-Carlo state-of-the-art techniques, supported by theoretical guarantees.
The price of fairness (PoF) quantifies the optimality loss when applying fairness constraints to a problem. Examining the egalitarian PoF in bipartite matching markets, where agents belong to distinct groups, we exploit matroid and geometric tools to characterize fair matchings, optimal matchings, and their intersection. An adversarial analysis reveals that a PoF of 1 is always achievable for two groups, while for a greater number of groups, the PoF can be arbitrarily large.
Bâtiment IMAG (106) -
[Talk] Henri Lefebvre
[Talk] Henri Lefebvre
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January 19, 2024Who: Henri Lefebvre
When: Friday 19/01, 10:30-11:30
Where: Room 406
What: Exact Methods in Adjustable Robust Optimization
More: This talk will be dedicated to solving complex decision-making problems in which (part of) the input data is not known at decision time. More precisely, we will discuss problems in which a two-stage decision phase is at stake. In the first stage, decisions must be made without complete information about the input parameters, while in the second stage, additional decisions can be informed by revealed information regarding the parameters. More specifically, we will dive into the theoretical derivation and practical use of exact methods for such problems with particular emphasis on the challenging setting in which the second-stage decisions are solution of a mixed-integer non-linear problem.
Bâtiment IMAG (442) -
Martin Quinson. SmolPhone: a smartphone with energy limits
Martin Quinson. SmolPhone: a smartphone with energy limits
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February 15, 2024This talk explores the smartphone design space to achieve energy sobriety. At its core, the proposed platform has a compute-limited but energy efficient microcontroller (MCU). Another processor can be attached to execute legacy Linux applications. Some tasks are offloaded to the Wi-Fi network controller. Our design prefers an energy-efficient variant of the 4G cellular network, which is turned off as often as possible. We are in the process of designing a hardware evaluation board to implement the vision presented in this talk.Bâtiment IMAG (447)
- 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
