arXiv research

A new exploration strategy for contextual bandits reduces regret and is computationally efficient.

New algorithms reduce costly feature collection in bandits.

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

We study graph matching with correlated Gaussian features and find thresholds for exact recovery.

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…

Enhances GNNs with text features for better fake news detection.

New algorithms for model selection in linear contextual bandits without feature diversity conditions.

Study sparsity benefits in infinite feature contextual bandits.

The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-cra…

A new estimator learns sparse linear models with context-dependent coefficients.

Novel algorithm reduces feature inclusion in online decision-making.

A new algorithm learns from raw feature vectors using deep neural networks and UCB for exploration.

Study online pricing with contextual elasticity and heteroscedastic valuation.

New method tackles high-dimensional contextual bandits with flexible kernel models.

Two novel methods identify influential features in CMABs for better reward distribution.

CACTI improves tabular data imputation by leveraging missingness patterns and contextual information.

Elliptical Attention improves transformer performance by focusing on contextually relevant features.

EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.

Proposes a neural network for contextual regression.

Proposes a framework to extract ordered eigenfunctions from contextual kernels.

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development…

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

A new method for decision tree selection in recommendation systems.

Optimizes decision-making in dueling bandits with contextual features.

We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We…

The paper proposes a method to learn and leverage contextual preference distributions for better decision-making.

LLM-Lasso uses LLMs to improve feature selection in Lasso regression.

Many efficient algorithms with strong theoretical guarantees have been proposed for the contextual multi-armed bandit problem. However, applying these algorithms in practice can be difficult because they require domain expertise to build appropriate features and to tune their parameters. We propose a new method for the…

A new adversarial attack method using structured search and contextual bandits.

A new DRL model for intraday trading incorporating positional context.

We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analyze Bayesian oracles given the observed noisy features. Our Bayesian analysis reveals that the optimal hypothesis can significantly deviate f…

Algorithm improves decision-making with partially observed contexts using pretrained models.

We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on contexts, and learn based only on a reward signal from the action taken, thereby generating an exploration/exploitation trade-off. MELEE address…

Improved Thompson Sampling for high-dimensional sparse bandits.

LoCo-RLHF models diverse human feedback with contextual information.

This paper improves context-aware recommender systems by selecting and incorporating relevant low-dimensional contextual information.

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate of our posterior update rule to the true probability function in…

New algorithms improve contextual search in the presence of adversarial corruptions.

A collaborative algorithm reduces regret in federated linear contextual bandits.

In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the con…

Two algorithms address limited adaptivity in generalized linear contextual bandits.

Algorithm minimizes regret in dueling bandits with contextualized utilities.

Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total reward over the inputs. Contextual bandits are commonly used to solve recommendation or ranking problems. This paper considers a learning s…

Neural Thompson Sampling uses deep neural networks for contextual bandit problems.

Spotify improves content mix using contextual bandits.

We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards. Using a fast inference procedure with Polya-Gamma distributed augmentation variables, we propose an improved version of Thomp…

Unified framework for ensemble sampling in nonlinear contextual bandits with provable regret bounds.

Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.