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We consider a setting where a system learns to rank a fixed set of $m$ items. The goal is produce good item rankings for users with diverse interests who interact online with the system for $T$ rounds. We consider a novel top-$1$ feedback…

Machine Learning · Computer Science 2016-08-24 Sougata Chaudhuri , Ambuj Tewari

We revisit the study of optimal regret rates in bandit combinatorial optimization---a fundamental framework for sequential decision making under uncertainty that abstracts numerous combinatorial prediction problems. We prove that the…

Machine Learning · Computer Science 2017-02-27 Alon Cohen , Tamir Hazan , Tomer Koren

We propose an algorithm for stochastic and adversarial multiarmed bandits with switching costs, where the algorithm pays a price $\lambda$ every time it switches the arm being played. Our algorithm is based on adaptation of the Tsallis-INF…

Machine Learning · Computer Science 2021-02-22 Chloé Rouyer , Yevgeny Seldin , Nicolò Cesa-Bianchi

We investigate the problem of cumulative regret minimization for individual sequence prediction with respect to the best expert in a finite family of size K under limited access to information. We assume that in each round, the learner can…

Statistics Theory · Mathematics 2022-10-06 El Mehdi Saad , G. Blanchard

Discounted-sum games provide a formal model for the study of reinforcement learning, where the agent is enticed to get rewards early since later rewards are discounted. When the agent interacts with the environment, she may regret her…

Computer Science and Game Theory · Computer Science 2018-11-20 Michaël Cadilhac , Guillermo A. Pérez , Marie van den Bogaard

In this paper, we broaden the horizon of online convex optimization (OCO), and consider multi-objective OCO, where there are $K$ distinct loss function sequences, and an algorithm has to choose its action at time $t$, before the $K$ loss…

Machine Learning · Computer Science 2026-02-11 Rahul Vaze , Sumiran Mishra

We consider the problem of asynchronous online combinatorial optimization on a network of communicating agents. At each time step, some of the agents are stochastically activated, requested to make a prediction, and the system pays the…

Machine Learning · Computer Science 2021-02-10 Riccardo Della Vecchia , Tommaso Cesari

We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and so the minimax…

Machine Learning · Computer Science 2022-10-14 Daniel Lee , Georgy Noarov , Mallesh Pai , Aaron Roth

We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy. We consider a general class of utility-based preference…

Machine Learning · Computer Science 2024-03-25 Aadirupa Saha , Hilal Asi

We study the problem of off-policy evaluation in the multi-armed bandit model with bounded rewards, and develop minimax rate-optimal procedures under three settings. First, when the behavior policy is known, we show that the Switch…

Machine Learning · Statistics 2021-01-20 Cong Ma , Banghua Zhu , Jiantao Jiao , Martin J. Wainwright

We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret of…

Machine Learning · Computer Science 2023-01-26 Tao Liu , Ruida Zhou , Dileep Kalathil , P. R. Kumar , Chao Tian

We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence in this setting is harder when the players are uncoupled, by proving a lower bound on…

Machine Learning · Computer Science 2026-04-17 Come Fiegel , Pierre Menard , Tadashi Kozuno , Michal Valko , Vianney Perchet

We study the $K$-armed contextual dueling bandit problem, a sequential decision making setting in which the learner uses contextual information to make two decisions, but only observes \emph{preference-based feedback} suggesting that one…

Machine Learning · Computer Science 2021-11-25 Aadirupa Saha , Akshay Krishnamurthy

Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of…

Machine Learning · Computer Science 2022-06-09 Sarah Sachs , Hédi Hadiji , Tim van Erven , Cristóbal Guzmán

In two-player zero-sum games, the learning dynamic based on optimistic Hedge achieves one of the best-known regret upper bounds among strongly-uncoupled learning dynamics. With an appropriately chosen learning rate, the social and…

Machine Learning · Computer Science 2025-10-14 Taira Tsuchiya

We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets that improve over the best-known regret bounds for any number…

Data Structures and Algorithms · Computer Science 2020-02-19 Hossein Esfandiari , Amin Karbasi , Abbas Mehrabian , Vahab Mirrokni

In online learning the performance of an algorithm is typically compared to the performance of a fixed function from some class, with a quantity called regret. Forster proposed a last-step min-max algorithm which was somewhat simpler than…

Machine Learning · Computer Science 2013-01-28 Edward Moroshko , Koby Crammer

We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of…

Machine Learning · Computer Science 2022-10-18 Viktor Bengs , Eyke Hüllermeier

We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual multi-armed bandit (CMAB) with general offline function approximation. Our algorithm is both…

Machine Learning · Computer Science 2026-02-17 Orin Levy , Yishay Mansour

We consider a bandit optimization problem for nonconvex and non-smooth functions, where in each trial the loss function is the sum of a linear function and a small but arbitrary perturbation chosen after observing the player's choice. We…

Machine Learning · Computer Science 2026-01-07 Zhuoyu Cheng , Kohei Hatano , Eiji Takimoto