arXiv research

Tr-LinUCB reduces regret in stochastic linear bandits by truncating exploration.

Proposes a new algorithm for non-stationary bandits.

PSI-LinUCB improves scalability for large recommender systems.

New algorithm reduces regret with diverse contexts in bandits.

New action poisoning attacks improve LinUCB's performance by changing action signals.

LinUCB algorithm handles adaptive sampling biases for linear bandits.

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a nove…

Two-stage recommender systems struggle with exploration, leading to linear regret.

Efficient inference for adaptive data with directional stability condition.

Novel Bayesian approach for non-stationary linear contextual bandits.

We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice. The first approach replaces optimism with a simple randomization when using confidence sets. The second one adds random perturbations to its current estimate before maximizing the expe…

Transformers learn to make decisions in new contexts from offline data.

A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded. We present a new technique that has the empirical and computational advantages of realizability-based approaches combined with the flexibility of agnostic methods. Our algorit…

New algorithms solve linear bandits in high dimensions efficiently.

BerlinUCB learns from episodic rewards in nonstationary contexts.

We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, …

New ranking algorithms improve online content delivery by learning from click data.

New algorithm reduces online learning regret by exploiting historical invariances.

Paper addresses federated contextual bandits with encryption.

In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-…

Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.

New algorithm improves online learning under performance constraints.

Combines offline causal inference and online bandit learning for better decision-making.

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…

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 …

We study reinforcement learning (RL) in high dimensional episodic Markov decision processes (MDP). We consider value-based RL when the optimal Q-value is a linear function of d-dimensional state-action feature representation. For instance, in deep-Q networks (DQN), the Q-value is a linear function of the feature repres…

We consider a stochastic linear bandit problem with multiple users, where the relationship between users is captured by an underlying graph and user preferences are represented as smooth signals on the graph. We introduce a novel bandit algorithm where the smoothness prior is imposed via the random-walk graph Laplacian…

Algorithm learns optimal coordination for strategic agents in uncertain settings.

Balances and eliminates base algorithms in bandits and RL to bound total regret.

This study presents two new algorithms for solving linear stochastic bandit problems. The proposed methods use an approach from non-parametric statistics called bootstrapping to create confidence bounds. This is achieved without making any assumptions about the distribution of noise in the underlying system. We present…

A distributed algorithm reduces communication cost in linear bandits to near-optimal levels.

Statistical inference for misspecified contextual bandits is challenging due to adaptivity issues.

Online learning algorithms require to often recompute least squares regression estimates of parameters. We study improving the computational complexity of such algorithms by using stochastic gradient descent (SGD) type schemes in place of classic regression solvers. We show that SGD schemes efficiently track the true s…

A framework for auto-tuning hyper-parameters in contextual bandit algorithms.

This paper tackles bandit optimization with a new pairwise comparison oracle for unknown strongly concave functions.

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked unifo…