1 citations · 1 across the 4 of their papers we have counts for
5 papers
ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning
Amirhossein Tighkhorshid, Zahra Dehghanian, Gholamali Aminian +2
Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remains expensive. Typically, this requires…
Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
Armin Behnamnia, Gholamali Aminian, Alireza Aghaei +3
Off-policy learning and evaluation leverage logged bandit feedback datasets, which contain context, action, propensity score, and feedback for each data point. These scenarios face…
Semi-pessimistic Reinforcement Learning
Jin Zhu, Xin Zhou, Jiaang Yao +5
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected data. However, it faces challenges of distributional shift, where the learned policy may enco…
Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework
Lan Luo, Chengchun Shi, Jitao Wang +2
Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there a…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…