arXiv Machine Learning
Jul 13
Adaptive Bayes exactly tracks information over intrinsic time
arXiv:2607. 08789v1 Announce Type: new Abstract: Bayesian and multiplicative-weights updates reweight experts, models, or actions from sequential feedback.
The concentration game: Bayesian updating, regret, and information
arXiv:2608. 18061v1 Announce Type: new Abstract: We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights regret at once, and supplies the comparator-class variational form that a wide class of concentration phenomena share.
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arXiv Machine Learning
Jun 19
Indexed Bellman Information Complexity
arXiv:2606. 11171v2 Announce Type: replace Abstract: We develop indexed Bellman information complexity, a representation-level theory of interactive decision making centered on information indices and reference histories.
arXiv Machine Learning
Aug 6
Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization
arXiv:2608. 04149v1 Announce Type: cross Abstract: Swap regret governs the rate at which uncoupled learning dynamics converge to correlated equilibria in multiplayer general-sum games.
arXiv Machine Learning
Jun 5
Regret Minimization with Adaptive Opponents in Repeated Games
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
arXiv AI
Jul 21
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
arXiv:2607. 16858v1 Announce Type: cross Abstract: Across environments with mixed sources of uncertainty, unsupervised reinforcement learning requires intrinsic motivation that does not precommit to a particular direction of surprise.
arXiv Machine Learning
Aug 11
Information Routing across Batch Boundaries: Memory--Batch Tradeoffs in Lipschitz Bandits
arXiv:2608. 07922v1 Announce Type: new Abstract: Adaptive learning needs both a state that preserves what observations imply and opportunities to act on that state.
arXiv Machine Learning
Aug 11
Regret, equilibrium, and learning in games: A guided tour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv AI
Jul 28
Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex
arXiv:2607. 23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al.
arXiv Machine Learning
Jul 23
Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
arXiv AI
Jun 9
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
arXiv:2606. 09802v1 Announce Type: cross Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time.
arXiv Machine Learning
Aug 3
Parameter-Free Heavy-Tailed Bandits
arXiv:2607. 29460v1 Announce Type: new Abstract: Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme outcomes can dominate performance.
arXiv Machine Learning
Jul 3
Online Resource Allocation with Continuous Random Consumption: Regret under Degeneracy
arXiv:2607. 02196v1 Announce Type: new Abstract: We study online resource allocation when both rewards and consumption sizes may be continuously distributed.