arXiv research

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

New algorithm reduces neural net error in contextual bandits.

A new algorithm for differential privacy in kernelized contextual bandits reduces error rate.

Proposes a framework to extract ordered eigenfunctions from contextual kernels.

A new algorithm tackles adversarial linear contextual bandits using kernelized loss functions.

Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries. In this work, by replacing the inner product function in the softmax layer, we explore the use of kernels …

Kernel method improves cooperative decision-making among agents.

New algorithm for differential private kernel bandits with improved regret bounds.

Kernelized bandit algorithm tackles adaptive contextual bandits with single-index models.

We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions' contexts and that the expected reward…

New algorithms use neural networks to minimize regret in contextual bandits.

Sharp policy value estimation for contextual bandits with unobserved confounders.

Explearn learns to explain predictions using Gaussian Processes.

Study sparsity benefits in infinite feature contextual bandits.

Unified framework for multi-user bandits using Laplacian kernels.

Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches hav…

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

New method tackles confounded bandit problems with dual instrumental variables.

Efficiently identifies good policies by choosing contexts for human feedback.

Single model estimates ensemble uncertainty efficiently.

New algorithm tackles non-linear utility in MNL bandits with $ilde{O}(\sqrt{T})$ regret.

New algorithms robust to adversarial data achieve optimal performance.

Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model eve…

ARM improves multivariate time series forecasting by better capturing series-wise relationships.

Study optimizes decisions in real-time using inexact simulation solutions.

Paper studies linear CMDPs, improving sample complexity for both models.

Paper introduces a neural network-based non-stationary influence kernel for complex event data.

An algorithm finds the most probable best solution in uncertain parameter settings.

New algorithms for neural bandits learn from context and arm features.

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.

New algorithms for optimizing functions with noisy feedback, even when the model is misspecified.

Proposes a neural network for contextual regression.

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

We consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the a…

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…