8 citations · 30 across the 9 of their papers we have counts for
9 papers
Adaptive Sampling for Discovery
Ziping Xu, Eunjae Shim, Ambuj Tewari +1
In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively l…
On the Statistical Benefits of Curriculum Learning
Ziping Xu, Ambuj Tewari
Curriculum learning (CL) is a commonly used machine learning training strategy. However, we still lack a clear theoretical understanding of CL's benefits. In this paper, we study t…
Bandit Algorithms for Precision Medicine
Yangyi Lu, Ziping Xu, Ambuj Tewari
The Oxford English Dictionary defines precision medicine as "medical care designed to optimize efficiency or therapeutic benefit for particular groups of patients, especially by us…
Safe Exploration by Solving Early Terminated MDP
Hao Sun, Ziping Xu, Meng Fang +4
Safe exploration is crucial for the real-world application of reinforcement learning (RL). Previous works consider the safe exploration problem as Constrained Markov Decision Proce…
Representation Learning Beyond Linear Prediction Functions
Ziping Xu, Ambuj Tewari
Recent papers on the theory of representation learning has shown the importance of a quantity called diversity when generalizing from a set of source tasks to a target task. Most o…
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns
Ziping Xu, Amirhossein Meisami, Ambuj Tewari
This paper studies the decision making problem with Funnel Structure. Funnel structure, a well-known concept in the marketing field, occurs in those systems where the decision make…
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Tarun Gogineni, Ziping Xu, Exequiel Punzalan +4
Molecular geometry prediction of flexible molecules, or conformer search, is a long-standing challenge in computational chemistry. This task is of great importance for predicting s…
Zeroth-Order Supervised Policy Improvement
Hao Sun, Ziping Xu, Yuhang Song +4
Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-orde…
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Ziping Xu, Ambuj Tewari
We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs. Assuming oracle a…