arXiv research

New algorithm for contextual combinatorial bandits with probabilistic arm triggering.

Paper proposes a method to control robots of different shapes efficiently.

Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor's outputs to its inputs. This paper uses a recent advance in the foundations of information theory to study the properties of local processors that u…

Proposes CLSM for better subsequence generation in music sequences.

BerlinUCB learns from episodic rewards in nonstationary contexts.

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …

Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing re…

CRAUM-Net improves salient object detection with context and uncertainty modeling.

Variance-Calibrated Modulation (VCM) addresses the likelihood trap in LLMs by reshaping the probability distribution before truncation.

New approach improves cross-domain recommendation for sparse target domains.

V-HMN integrates memory mechanisms for improved image recognition.

Contextualized ML learns context-dependent effects using deep learning.

Complex architectures of biological neural circuits, such as parallel processing pathways, has been behaviorally implicated in many cognitive studies. However, the theoretical consequences of circuit complexity on neural computation have only been explored in limited cases. Here, we introduce a mechanism by which direc…

IDS improves reinforcement learning with contextual information.

SADCBO optimizes contextual variables by balancing relevance and cost.

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

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

Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous w…

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.

CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.

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.

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.