activity
20192022
most citedOn the Convergence and Optimality of Policy Gradient for Markov Coherent Risk

5 citations · 5 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2022

Off-Policy Risk Assessment in Markov Decision Processes

Audrey Huang, Liu Leqi, Zachary Chase Lipton +1

Addressing such diverse ends as safety alignment with human preferences, and the efficiency of learning, a growing line of reinforcement learning research focuses on risk functiona…

cs.CY2021

When Curation Becomes Creation: Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators

Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton

Ever since social activity on the Internet began migrating from the wilds of the open web to the walled gardens erected by so-called platforms, debates have raged about the respons…

cs.LG2021

Off-Policy Risk Assessment in Contextual Bandits

Audrey Huang, Liu Leqi, Zachary C. Lipton +1

Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data. However, while the bandits literature has adopted a diverse set…

cs.LG20215 cited

On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk

Audrey Huang, Liu Leqi, Zachary C. Lipton +1

In order to model risk aversion in reinforcement learning, an emerging line of research adapts familiar algorithms to optimize coherent risk functionals, a class that includes cond…

cs.LG2020

Rebounding Bandits for Modeling Satiation Effects

Liu Leqi, Fatma Kilinc-Karzan, Zachary C. Lipton +1

Psychological research shows that enjoyment of many goods is subject to satiation, with short-term satisfaction declining after repeated exposures to the same item. Nevertheless, p…

cs.LG2019

Game Design for Eliciting Distinguishable Behavior

Fan Yang, Liu Leqi, Yifan Wu +4

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such trai…