Search Results for author:
Found 13 papers, 3 papers with code
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics
In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability.
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects
Randomized experiments (a. k. a.
Split Localized Conformal Prediction
The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings.
Robust Imitation Learning from Corrupted Demonstrations
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers.
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning
The benefit of learning the operator is that we can incorporate any new reward function as input and attain its corresponding value function in a zero-shot manner.
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.
Off-Policy Interval Estimation with Lipschitz Value Iteration
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of the new policy.
PENet: Object Detection using Points Estimation in Aerial Images
Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance.
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference.
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Our method is doubly robust in that the bias vanishes when either the density ratio or the value function estimation is perfect.
Multiple Learning for Regression in big data
Results showed our approaches can achieve closed-form solutions of multiple models at the cost of half training time of the traditional methods for a single model.
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy.
