arXiv research

New algorithm reduces switching costs in multinomial logit bandit problems.

New algorithms tackle adversarial combinatorial bandits with switching costs.

SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.

Algorithm for bandits with switching costs achieves optimal regret bounds.

Study of multi-armed bandits with state-switching rewards using Markov models.

New algorithm reduces regret for many bandit algorithms with logarithmic dependence on number of algorithms.

We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given switching budget. For this problem, we prove matching upper and lower bounds on the optimal (i.e., minimax) regret, and provide efficient rate-o…

New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.

Paper tackles non-stationary bandits with various examples.

New algorithm tackles non-stationary combinatorial semi-bandit problems with optimal regret bounds.

We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full-information and bandit feedback. We measure the player's performance using a new notion of regret, also known as policy regret, which better captures the adversary's adaptiveness…

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

FTRL algorithm with negative entropy regularizer achieves best-of-three-world results for linear bandits.

New method tracks significant arm switches to improve bandit algorithms.

We study the adversarial multi-armed bandit problem where partial observations are available and where, in addition to the loss incurred for each action, a \emph{switching cost} is incurred for shifting to a new action. All previously known results incur a factor proportional to the independence number of the feedback …

Study on adaptivity constraints in linear contextual bandits with optimal design.

This paper proposes a general framework of multi-armed bandit (MAB) processes by introducing a type of restrictions on the switches among arms evolving in continuous time. The Gittins index process is constructed for any single arm subject to the restrictions on switches and then the optimality of the corresponding Git…

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

New findings show many popular bandit algorithms are unstable, contradicting minimax optimality.

We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to achieving typical switching regret. Specifically, for the classical expert problem with $K$ actions and $T$ rounds, using our framework we dev…

New DR-IC estimator reduces bias and variance in OPE.

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous works studied the expected regret of the le…

Optimal algorithm reduces regret in adversarial bandit problem with multiple plays.

New algorithm achieves both static and dynamic regret optimally against an oblivious adversary for deterministic losses.

New algorithm tackles adversarial bandits with arbitrary strategies.

Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so often. We may thus want to only change a policy when it is probable that the change is beneficial. In cases that a policy is a threshold on …

Motivated by recommendation problems in music streaming platforms, we propose a nonstationary stochastic bandit model in which the expected reward of an arm depends on the number of rounds that have passed since the arm was last pulled. After proving that finding an optimal policy is NP-hard even when all model paramet…

Efficient algorithms for contextual slate bandits with limited adaptivity.

Algorithm provides online learning guarantees against general comparators in full and bandit feedback.

We study the off-policy evaluation problem---estimating the value of a target policy using data collected by another policy---under the contextual bandit model. We consider the general (agnostic) setting without access to a consistent model of rewards and establish a minimax lower bound on the mean squared error (MSE).…

New algorithm minimizes expert selection regret in partial bandit feedback.

New algorithm handles bandit problems under translations and scales.

A new framework tunes hyperparameters in real-time for contextual bandits.

A new algorithm for cryo-EM data collection that balances reward and latency.

Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to a…

Study tackles non-stationary bandit convex optimization with new algorithms.

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min\{\sqrt{ST}, Δ^{\frac{1}{3}}T^{\frac{2}{3}}\})$ for a contextual bandit problem with $T$ rounds, $S$ switches and $Δ$ total var…

Robust algorithm optimizes corrupted Gaussian process bandits.

Most contextual bandit algorithms minimize regret against the best fixed policy, a questionable benchmark for non-stationary environments that are ubiquitous in applications. In this work, we develop several efficient contextual bandit algorithms for non-stationary environments by equipping existing methods for i.i.d. …

Improves text classification on new domains using distance-based measures and dynamic domain selection.

We consider the exploration-exploitation tradeoff in linear quadratic (LQ) control problems, where the state dynamics is linear and the cost function is quadratic in states and controls. We analyze the regret of Thompson sampling (TS) (a.k.a. posterior-sampling for reinforcement learning) in the frequentist setting, i.…

Paper optimizes experimental design for estimating treatment effect.

A new framework for risk-aware multi-armed bandits tackles volatile environments.

Adaptive KL-UCB algorithm for Markov and i.i.d. rewards.

The multi-armed bandit (MAB) problem is a classic example of the exploration-exploitation dilemma. It is concerned with maximising the total rewards for a gambler by sequentially pulling an arm from a multi-armed slot machine where each arm is associated with a reward distribution. In static MABs, the reward distributi…

Study non-stationary online auctions with semi-bandit feedback.

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $i$ to perform, and receives some reward $r_{i,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{i,t}(c)$, the learner also learns the values of $r_{i,t}(c…

New method optimizes policies in non-stationary environments.