activity
20192026
most citedBayesian Q-learning With Imperfect Expert Demonstrations

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR2026

GenPage: Towards End-to-End Generative Homepage Construction at Netflix

Lequn Wang, Jiangwei Pan, Linas Baltrunas +1

We present GenPage, an end-to-end generative approach to Netflix homepage construction that replaces the traditional multi-stage recommender stack with a single transformer. GenPag…

cs.LG2025

Average-DICE: Stationary Distribution Correction by Regression

Fengdi Che, Bryan Chan, Chen Ma +1

Off-policy policy evaluation (OPE), an essential component of reinforcement learning, has long suffered from stationary state distribution mismatch, undermining both stability and…

cs.LG2024

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation

Fengdi Che, Chenjun Xiao, Jincheng Mei +6

We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation…

cs.LG2023

Correcting discount-factor mismatch in on-policy policy gradient methods

Fengdi Che, Gautham Vasan, A. Rupam Mahmood

The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution…

cs.LG2022★ 3 cited

Bayesian Q-learning With Imperfect Expert Demonstrations

Fengdi Che, Xiru Zhu, Doina Precup +2

Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information. We propose a novel algor…

eess.IV2019★ 1 cited

Detecting GAN generated errors

Xiru Zhu, Fengdi Che, Tianzi Yang +3

Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sa…