arXiv research

Study identifies methods for handling sparse data in web settings with contextual bandits.

A new algorithm for personalized recommendations adapts to changing user interests.

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's…

Paper proposes a hybrid model for financial time series prediction using sentiment analysis.

Deep neural networks (DNNs) provide high image classification accuracy, but experience significant performance degradation when perturbation from various sources are present in the input. The lack of resilience to input perturbations makes DNN less reliable for systems interacting with physical world such as autonomous…

A new method combines online and offline learning to tackle contextual bandits with missing action support.

A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode…

CAT toolkit combines hybrid and E2E approaches for efficient speech recognition.

Framework for robust decision making in changing environments with privacy constraints.

New algorithm reduces online learning error for unknown feature distributions.

Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics. In this paper, we s…

Unified hybrid RL algorithm improves online RL performance with offline data.

Machine learning algorithms such as linear regression, SVM and neural network have played an increasingly important role in the process of scientific discovery. However, none of them is both interpretable and accurate on nonlinear datasets. Here we present contextual regression, a method that joins these two desirable …

CATA++ improves scientific article recommendations by learning latent spaces with attention.

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

HMS-BERT detects cyberbullying in multiple languages and labels.

CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.

H-STGCN predicts traffic using navigation data and improves accuracy.

Paper addresses RLHF alignment challenges with novel algorithms.

Contextualized ML learns context-dependent effects using deep learning.

IDS improves reinforcement learning with contextual information.

This paper explores how LLMs can improve pipeline-based conversational agents.

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.

Defines non-parabolic curves in spatial hybrid space with applications.

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 algorithm reduces contextual bandits to efficient regression.

New method for contextual bandit with missing rewards.

GLCB uses Gated Linear Networks for online contextual bandits.

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.