arXiv research

Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.

Kernel $ε$-Greedy optimizes multi-armed bandits with covariates for sub-linear regret.

In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regr…

In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property. Specifically, a sequence of monotone DR-submodular objective functions $\{f_t(x)\}_{t=1…

Paper tackles LDP bandits learning with improved results and sub-linear regret.

First robust bandit algorithm for contextual bandits with sub-linear regret.

The paper tackles minimax optimality in continuum contextual bandits with Hölder continuity.

New algorithm tackles adversarial corruption in Lipschitz bandits with sub-linear regret.

This paper addresses robust CBs for linear SEMs with model fluctuations.

In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy …

New algorithms reduce slate bandit regret for large slates, outperforming existing methods.

New algorithms for planning with adversarial changes in costs.

Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, su…

We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the un…

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

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

Study designs steering rewards for MFGs with unknown dynamics and model uncertainty.

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high…

New algorithm reduces risk in online games with limited feedback.

A multi-user multi-armed bandit (MAB) framework is used to develop algorithms for uncoordinated spectrum access. The number of users is assumed to be unknown to each user. A stochastic setting is first considered, where the rewards on a channel are the same for each user. In contrast to prior work, it is assumed that t…

A new algorithm improves stochastic linear bandit performance using residual bootstrap.

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinfo…

New algorithms for fair item allocation with limited copies.

Develops algorithms for CCBs with non-linear costs, improving safety and performance.

Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available for saddle-point optimization has focused on the offline setting. In this paper, we study nonstationar…

Algorithm optimizes spectrum access for dynamic multi-user environments.

Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.

New algorithm reduces regret in CBs with time-varying models.

We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and establish a near-linear lower bound on the regret incurred by it under the bandit model with Bernoulli rewards. We show that NPB with an approp…

This paper improves GP-UCB by using a shifted exponential distribution for confidence parameters.

A new algorithm for restless bandits handles long-range dependencies.

New algorithm SELECT minimizes satisficing regret in bandits.

New algorithms improve online prediction from experts with privacy constraints.

Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algorithms over the unknown reward distributions and prove a sub-linear, $O(\sqrt{T}\log T)$, regret bound for a variant of Thompson sampling. Our…

EBUCB framework achieves optimal regret with bounded approximate inference error.

New RL algorithm optimizes policies with bandit feedback, matching previous bounds.

Algorithm reduces long-term policy regret in ML decision-making.

We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the d…

LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.

Algorithm finds optimal regularizers for online linear optimization.

Paper develops a robust Bayesian optimization method for noisy zeroth-order settings.

Study online learning with individual fairness without known similarity measure.

Avare improves optimization and sampling with adaptive importance sampling.

Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt to solve this challenge in the context of decentralized learning in multi-agent linear-quadratic (LQ) dynamical systems. We begin with a simp…

New algorithm for online learning in episodic MDPs with convex objectives.

Study contextual online pricing with biased offline data, achieving optimal regret bounds.