5 papers · 1 filter
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
Vikram Krishnamurthy, Luke Snow
Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses. Adaptive IRL aims to reconstruct the loss function of a forward le…
Why Most Optimism Bandit Algorithms Have the Same Regret Analysis: A Simple Unifying Theorem
Vikram Krishnamurthy
Several optimism-based stochastic bandit algorithms -- including UCB, UCB-V, linear UCB, and finite-arm GP-UCB -- achieve logarithmic regret using proofs that, despite superficial…
Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
Vikram Krishnamurthy
This monograph, spanning three chapters, explores Inverse Reinforcement Learning (IRL). The first two chapters view inverse reinforcement learning (IRL) through the lens of reveale…
Efficient Neural SDE Training using Wiener-Space Cubature
Luke Snow, Vikram Krishnamurthy
A neural stochastic differential equation (SDE) is an SDE with drift and diffusion terms parametrized by neural networks. The training procedure for neural SDEs consists of optimiz…
Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
Luke Snow, Vikram Krishnamurthy
We derive a minimax distributionally robust inverse reinforcement learning (IRL) algorithm to reconstruct the utility functions of a multi-agent sensing system. Specifically, we co…