Deep Hierarchy in Bandits. | 0 | 0.34 | 2022 |
Hierarchical Bayesian Bandits | 0 | 0.34 | 2022 |
Fixed-Budget Best-Arm Identification in Structured Bandits | 0 | 0.34 | 2022 |
Feature and Parameter Selection in Stochastic Linear Bandits. | 0 | 0.34 | 2022 |
Multi-Environment Meta-Learning in Stochastic Linear Bandits. | 0 | 0.34 | 2022 |
Thompson Sampling with a Mixture Prior | 0 | 0.34 | 2022 |
Mirror Descent Policy Optimization | 0 | 0.34 | 2022 |
Control-Aware Representations for Model-based Reinforcement Learning | 0 | 0.34 | 2021 |
A review of uncertainty quantification in deep learning: Techniques, applications and challenges | 11 | 0.80 | 2021 |
Variational Model-based Policy Optimization. | 0 | 0.34 | 2021 |
Pid Accelerated Value Iteration Algorithm | 0 | 0.34 | 2021 |
Deep Bayesian Quadrature Policy Optimization | 0 | 0.34 | 2021 |
Control-Aware Representations for Model-based Reinforcement Learning. | 0 | 0.34 | 2021 |
Neural Lyapunov Redesign. | 0 | 0.34 | 2021 |
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. | 0 | 0.34 | 2020 |
Adaptive Sampling for Estimating Probability Distributions | 0 | 0.34 | 2020 |
Predictive Coding for Locally-Linear Control | 0 | 0.34 | 2020 |
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control | 0 | 0.34 | 2020 |
Multi-Step Greedy Reinforcement Learning Algorithms | 0 | 0.34 | 2020 |
Lyapunov-based Safe Policy Optimization for Continuous Control. | 0 | 0.34 | 2019 |
Perturbed-History Exploration in Stochastic Multi-Armed Bandits. | 0 | 0.34 | 2019 |
Perturbed-History Exploration in Stochastic Linear Bandits. | 0 | 0.34 | 2019 |
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. | 0 | 0.34 | 2019 |
Active Learning for Binary Classification with Abstention. | 0 | 0.34 | 2019 |
Binary Classification with Bounded Abstention Rate. | 0 | 0.34 | 2019 |
Randomized Exploration in Generalized Linear Bandits. | 0 | 0.34 | 2019 |
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. | 0 | 0.34 | 2018 |
Optimizing over a Restricted Policy Class in Markov Decision Processes. | 2 | 0.41 | 2018 |
Proximal gradient temporal difference learning: stable reinforcement learning with polynomial sample complexity | 0 | 0.34 | 2018 |
PAC Bandits with Risk Constraints. | 0 | 0.34 | 2018 |
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. | 0 | 0.34 | 2018 |
Disentangling Dynamics and Content for Control and Planning. | 0 | 0.34 | 2017 |
Model-Independent Online Learning for Influence Maximization. | 2 | 0.40 | 2017 |
Conservative Contextual Linear Bandits. | 0 | 0.34 | 2017 |
Active Learning for Accurate Estimation of Linear Models. | 2 | 0.37 | 2017 |
Bottleneck Conditional Density Estimation. | 0 | 0.34 | 2017 |
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. | 2 | 0.36 | 2017 |
Online Learning to Rank in Stochastic Click Models. | 7 | 0.47 | 2017 |
Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display. | 0 | 0.34 | 2017 |
Automated Data Cleansing through Meta-Learning. | 0 | 0.34 | 2017 |
Sequential Decision Making With Coherent Risk. | 4 | 0.41 | 2017 |
Online Learning to Rank in Stochastic Click Models. | 0 | 0.34 | 2017 |
Diffusion Independent Semi-Bandit Influence Maximization. | 0 | 0.34 | 2017 |
Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs | 4 | 0.45 | 2016 |
Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem. | 0 | 0.34 | 2016 |
Graphical Model Sketch. | 2 | 0.40 | 2016 |
Proximal Gradient Temporal Difference Learning Algorithms. | 1 | 0.37 | 2016 |
Bayesian Policy Gradient and Actor-Critic Algorithms | 3 | 0.40 | 2016 |
Analysis of Classification-based Policy Iteration Algorithms. | 29 | 1.27 | 2016 |
Regularized Policy Iteration with Nonparametric Function Spaces. | 1 | 0.34 | 2016 |