Finite-Sample Bounds for Adaptive Inverse Reinforcement Learning using Passive Langevin Dynamics
Fuente:
arXiv
Saved in:
| Main Authors: | Snow, Luke, Krishnamurthy, Vikram |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
by: Krishnamurthy, Vikram, et al.
Published: (2026)
by: Krishnamurthy, Vikram, et al.
Published: (2026)
Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
by: Snow, Luke, et al.
Published: (2024)
by: Snow, Luke, et al.
Published: (2024)
Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
by: Krishnamurthy, Vikram
Published: (2025)
by: Krishnamurthy, Vikram
Published: (2025)
Efficient Neural SDE Training using Wiener-Space Cubature
by: Snow, Luke, et al.
Published: (2025)
by: Snow, Luke, et al.
Published: (2025)
Malliavin Calculus with Weak Derivatives for Counterfactual Stochastic Optimization
by: Krishnamurthy, Vikram, et al.
Published: (2025)
by: Krishnamurthy, Vikram, et al.
Published: (2025)
Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
by: Snow, Luke, et al.
Published: (2025)
by: Snow, Luke, et al.
Published: (2025)
Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks
by: Snow, Luke, et al.
Published: (2025)
by: Snow, Luke, et al.
Published: (2025)
Finite Sample and Large Deviations Analysis of Stochastic Gradient Algorithm with Correlated Noise
by: Yin, George, et al.
Published: (2024)
by: Yin, George, et al.
Published: (2024)
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization
by: Jain, Adit, et al.
Published: (2024)
by: Jain, Adit, et al.
Published: (2024)
Data-Driven Mechanism Design using Multi-Agent Revealed Preferences
by: Snow, Luke, et al.
Published: (2024)
by: Snow, Luke, et al.
Published: (2024)
Efficient Counterfactual Estimation of Conditional Greeks via Malliavin-based Weak Derivatives
by: Krishnamurthy, Vikram, et al.
Published: (2026)
by: Krishnamurthy, Vikram, et al.
Published: (2026)
Why Most Optimism Bandit Algorithms Have the Same Regret Analysis: A Simple Unifying Theorem
by: Krishnamurthy, Vikram
Published: (2025)
by: Krishnamurthy, Vikram
Published: (2025)
LLMs as High-Dimensional Nonlinear Autoregressive Models with Attention: Training, Alignment and Inference
by: Krishnamurthy, Vikram
Published: (2026)
by: Krishnamurthy, Vikram
Published: (2026)
Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning
by: Krishnamurthy, Vikram, et al.
Published: (2024)
by: Krishnamurthy, Vikram, et al.
Published: (2024)
Interacting Large Language Model Agents. Interpretable Models and Social Learning
by: Jain, Adit, et al.
Published: (2024)
by: Jain, Adit, et al.
Published: (2024)
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
by: Song, Yuda, et al.
Published: (2024)
by: Song, Yuda, et al.
Published: (2024)
Finite Sample Bounds for Learning with Score Matching
by: Smedira, Devin, et al.
Published: (2026)
by: Smedira, Devin, et al.
Published: (2026)
Second Order Ensemble Langevin Method for Sampling and Inverse Problems
by: Liu, Ziming, et al.
Published: (2022)
by: Liu, Ziming, et al.
Published: (2022)
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
by: Bourdrez, Constant, et al.
Published: (2026)
by: Bourdrez, Constant, et al.
Published: (2026)
On the Hardness of Sampling from Mixture Distributions via Langevin Dynamics
by: Cheng, Xiwei, et al.
Published: (2024)
by: Cheng, Xiwei, et al.
Published: (2024)
Detecting Structural Shifts in Multivariate Hawkes Processes with Fréchet Statistics
by: Luo, Rui, et al.
Published: (2023)
by: Luo, Rui, et al.
Published: (2023)
On The Sample Complexity Bounds In Bilevel Reinforcement Learning
by: Gaur, Mudit, et al.
Published: (2025)
by: Gaur, Mudit, et al.
Published: (2025)
Direct Soft-Policy Sampling via Langevin Dynamics
by: Ki, Donghyeon, et al.
Published: (2026)
by: Ki, Donghyeon, et al.
Published: (2026)
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
by: Silvestri, Gianluigi, et al.
Published: (2024)
by: Silvestri, Gianluigi, et al.
Published: (2024)
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
by: Chen, Letian, et al.
Published: (2022)
by: Chen, Letian, et al.
Published: (2022)
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
by: Rajpal, Rajit, et al.
Published: (2025)
by: Rajpal, Rajit, et al.
Published: (2025)
Inferring Group Intent as a Cooperative Game. An NLP-based Framework for Trajectory Analysis
by: Zhang, Yiming, et al.
Published: (2025)
by: Zhang, Yiming, et al.
Published: (2025)
Inverse Reinforcement Learning without Reinforcement Learning
by: Swamy, Gokul, et al.
Published: (2023)
by: Swamy, Gokul, et al.
Published: (2023)
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by: Goddla, Vikram
Published: (2024)
by: Goddla, Vikram
Published: (2024)
On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
by: Morin, Sacha, et al.
Published: (2026)
by: Morin, Sacha, et al.
Published: (2026)
FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers
by: Park, Jonghyun, et al.
Published: (2025)
by: Park, Jonghyun, et al.
Published: (2025)
Finite-Sample Analysis of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
by: Chen, Suei-Wen, et al.
Published: (2024)
by: Chen, Suei-Wen, et al.
Published: (2024)
Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning
by: Xu, Yang, et al.
Published: (2025)
by: Xu, Yang, et al.
Published: (2025)
Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds
by: Jiang, Yuwen
Published: (2026)
by: Jiang, Yuwen
Published: (2026)
Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity
by: Maran, Davide, et al.
Published: (2024)
by: Maran, Davide, et al.
Published: (2024)
Distributional Inverse Reinforcement Learning
by: Wu, Feiyang, et al.
Published: (2025)
by: Wu, Feiyang, et al.
Published: (2025)
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
by: Moradipari, Ahmadreza, et al.
Published: (2023)
by: Moradipari, Ahmadreza, et al.
Published: (2023)
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
by: Wang, Shengbo, et al.
Published: (2023)
by: Wang, Shengbo, et al.
Published: (2023)
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
by: Hassani, Hossein, et al.
Published: (2024)
by: Hassani, Hossein, et al.
Published: (2024)
Sampling and estimation on manifolds using the Langevin diffusion
by: Bharath, Karthik, et al.
Published: (2023)
by: Bharath, Karthik, et al.
Published: (2023)
Similar Items
-
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
by: Krishnamurthy, Vikram, et al.
Published: (2026) -
Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
by: Snow, Luke, et al.
Published: (2024) -
Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
by: Krishnamurthy, Vikram
Published: (2025) -
Efficient Neural SDE Training using Wiener-Space Cubature
by: Snow, Luke, et al.
Published: (2025) -
Malliavin Calculus with Weak Derivatives for Counterfactual Stochastic Optimization
by: Krishnamurthy, Vikram, et al.
Published: (2025)