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08/03/2023
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL
In reinforcement learning (RL), a reward function is often assumed at th...
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06/27/2023
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Non-asymptotic convergence analysis of quasi-Newton methods has gained a...
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06/09/2023
Ada-NAV: Adaptive Trajectory-Based Sample Efficient Policy Learning for Robotic Navigation
Reinforcement learning methods, while effective for learning robotic nav...
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06/08/2023
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels
Learning with noisy labels is an important topic for scalable training i...
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05/27/2023
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
We investigate safe multi-agent reinforcement learning, where agents see...
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05/26/2023
Sharpened Lazy Incremental Quasi-Newton Method
We consider the finite sum minimization of n strongly convex and smooth ...
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02/15/2023
Scalable Multi-Agent Reinforcement Learning with General Utilities
We study the scalable multi-agent reinforcement learning (MARL) with gen...
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01/28/2023
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Many existing reinforcement learning (RL) methods employ stochastic grad...
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01/28/2023
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (R...
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08/24/2022
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path
We consider online reinforcement learning in Mean-Field Games. In contra...
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06/22/2022
: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In federated learning (FL), the objective of collaboratively learning a ...
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06/12/2022
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradien...
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06/02/2022
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
In this work, we propose a novel Kernelized Stein Discrepancy-based Post...
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03/02/2022
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation
We present the first distributed optimization algorithm with lazy commun...
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01/28/2022
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
We focus on parameterized policy search for reinforcement learning over ...
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01/21/2022
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search
We develop a new measure of the exploration/exploitation trade-off in in...
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01/18/2022
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
A fundamental challenge in Bayesian inference is efficient representatio...
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10/22/2021
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
In tabular multi-agent reinforcement learning with average-cost criterio...
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10/12/2021
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
Multi-agent mapping is a fundamentally important capability for autonomo...
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09/13/2021
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Reinforcement learning is widely used in applications where one needs to...
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07/26/2021
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference
Gaussian processes (GPs) are a well-known nonparametric Bayesian inferen...
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06/15/2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Reinforcement learning is a framework for interactive decision-making wi...
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05/29/2021
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic
We posit a new mechanism for cooperation in multi-agent reinforcement le...
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03/30/2021
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression
We develop an online probabilistic metric-semantic mapping approach for ...
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03/26/2021
Composable Learning with Sparse Kernel Representations
We present a reinforcement learning algorithm for learning sparse non-pa...
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11/13/2020
Sparse Representations of Positive Functions via Projected Pseudo-Mirror Descent
We consider the problem of expected risk minimization when the populatio...
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09/10/2020
A Markov Decision Process Approach to Active Meta Learning
In supervised learning, we fit a single statistical model to a given dat...
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07/04/2020
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
In recent years, reinforcement learning (RL) systems with general goals ...
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07/02/2020
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
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04/23/2020
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck
Gaussian processes provide a framework for nonlinear nonparametric Bayes...
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04/09/2020
Policy Gradient using Weak Derivatives for Reinforcement Learning
This paper considers policy search in continuous state-action reinforcem...
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03/27/2020
Distributed Beamforming for Agents with Localization Errors
We consider a scenario in which a group of agents aim to collectively tr...
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03/23/2020
Efficient Gaussian Process Bandits by Believing only Informative Actions
Bayesian optimization is a framework for global search via maximum a pos...
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03/06/2020
Asynchronous and Parallel Distributed Pose Graph Optimization
We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASA...
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02/27/2020
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain
We study the estimation of risk-sensitive policies in reinforcement lear...
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10/18/2019
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation
Reinforcement learning, mathematically described by Markov Decision Prob...
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09/25/2019
Optimally Compressed Nonparametric Online Learning
Batch training of machine learning models based on neural networks is no...
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09/23/2019
Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates
In Bayesian inference, we seek to compute information about random varia...
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09/12/2019
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
An open challenge in supervised learning is conceptual drift: a data poi...
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08/01/2019
Adaptive Kernel Learning in Heterogeneous Networks
We consider the framework of learning over decentralized networks, where...
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06/19/2019
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies
Policy gradient (PG) methods are a widely used reinforcement learning me...
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04/19/2018
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems
We consider Markov Decision Problems defined over continuous state and a...
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10/11/2017
Decentralized Online Learning with Kernels
We consider multi-agent stochastic optimization problems over reproducin...
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06/15/2016
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
We consider learning problems over training sets in which both, the numb...
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05/03/2016
