arXiv research

Contextualized ML learns context-dependent effects using deep learning.

IDS improves reinforcement learning with contextual information.

SADCBO optimizes contextual variables by balancing relevance and cost.

The bundle approach and n-contextuality reveal quantum model contextuality.

Proposes a neural network for contextual regression.

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

Transformers model contextual relations using probabilistic measures, revealing their expressive power.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

The study explores whether model selection guarantees apply to contextual bandits.

Faster algorithm reduces contextual bandit regret with fewer offline regression calls.

Efficiently handles contextual bandits with diffusion models.

This study analyzes how RNNs process context in sentiment analysis.

Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.

CADR estimator improves inference for contextual bandit data.

New algorithm learns optimal exploration parameters for contextual bandits.

The paper tackles learning varying DAG structures based on contextual features.

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual at…

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

New algorithm improves learning efficiency in multi-task contextual bandits.

We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime…

New algorithm for contextual dueling bandits achieves nearly optimal regret.

New method for contextual bandit with missing rewards.

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

LMC-TS uses MCMC for efficient posterior sampling in contextual bandits.

New method for contextual bandits with corrupted context.

Proposes a new algorithm for graph-based semi-parametric contextual bandits.

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…

New algorithm estimates treatment effects for more efficient contextual bandits.

New algorithm for contextual bandits with linear constraints using regression.

Contextual information helps identify the best arm more efficiently.

A simple algorithm reduces federated contextual linear bandits' regret efficiently.

A new algorithm avoids worst-case outcomes in risky contexts.

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We study a consideration for the expl…

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

We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co…

Bayesian method infers contextual bandit policies robustly.

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

This paper achieves optimal regret bounds for locally private linear contextual bandit.

Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine. At the same time, there are, as of yet, surprisingly few options…

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…

Study nonparametric contextual bandits with batched updates, achieving optimal regret.

Paper addresses federated contextual bandits with encryption.

Novel approach to contextual bandits using self-supervised learning.

Proposes sampling from reverse diffusion posteriors for contextual bandits.

Locally-contextual CRFs improve sequence labeling performance.

Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven (principled) exploration, or non-linearly, by using epsilon-greedy exploration policies. Here we present a deep learning framework for cont…

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual …

COPP provides reliable intervals for outcomes under a new policy in contextual bandits.