Category: cs.GT
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Efficient Uncoupled Learning Dynamics with $tilde{O}!left(T^{-1/4}right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Efficient Uncoupled Learning Dynamics with $tilde{O}!left(T^{-1/4}right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback arXiv:2602.21436v1 Announce Type: new Abstract: In this paper, we study last-iterate convergence of learning algorithms in bilinear saddle-point problems, a preferable notion of convergence that captures the day-to-day behavior of learning dynamics. We focus on the challenging…
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On the Fundamental Impossibility of Hallucination Control in Large Language Models
On the Fundamental Impossibility of Hallucination Control in Large Language Models arXiv:2506.06382v1 Announce Type: new Abstract: This paper explains textbf{why it is impossible to create large language models that do not hallucinate and what are the trade-offs we should be looking for}. It presents a formal textbf{impossibility theorem} demonstrating that no inference mechanism can simultaneously…
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Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback
Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback arXiv:2505.13562v1 Announce Type: new Abstract: Learning in games is a fundamental problem in machine learning and artificial intelligence, with numerous applications~citep{silver2016mastering,schrittwieser2020mastering}. This work investigates two-player zero-sum matrix games with an unknown payoff matrix and bandit feedback, where each player observes their actions and the…
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DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information arXiv:2505.05842v1 Announce Type: cross Abstract: Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients to participate in OFL, it is crucial to offer appropriate incentives to offset…
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PAC Learning with Improvements
PAC Learning with Improvements arXiv:2503.03184v1 Announce Type: new Abstract: One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes $textit{at least}$ $1/epsilon$ samples to learn to error $epsilon$ (and more, if the classifier being learned is complex). However, suppose that data points are agents who have…
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Learning and Computation of $Phi$-Equilibria at the Frontier of Tractability
Learning and Computation of $Phi$-Equilibria at the Frontier of Tractability arXiv:2502.18582v1 Announce Type: new Abstract: $Phi$-equilibria — and the associated notion of $Phi$-regret — are a powerful and flexible framework at the heart of online learning and game theory, whereby enriching the set of deviations $Phi$ begets stronger notions of rationality. Recently, Daskalakis, Farina, Fishelson,…