9 citations · 21 across the 8 of their papers we have counts for
8 papers
Symbolic Regression via Control Variable Genetic Programming
Nan Jiang, Yexiang Xue
Learning symbolic expressions directly from experiment data is a vital step in AI-driven scientific discovery. Nevertheless, state-of-the-art approaches are limited to learning sim…
Adversarial Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
The policy represented by the deep neural network can overfit the spurious features in observations, which hamper a reinforcement learning agent from learning effective policy. Thi…
On the Value of Behavioral Representations for Dense Retrieval
Nan Jiang, Dhivya Eswaran, Choon Hui Teo +4
We consider text retrieval within dense representational space in real-world settings such as e-commerce search where (a) document popularity and (b) diversity of queries associate…
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance. In this paper, we propose Thinker, a bootstrapping metho…
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Yexiang Xue, Zhiyuan Li, Stefano Ermon +2
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of infe…
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
Yexiang Xue, Junwen Bai, Ronan Le Bras +8
High-Throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in materials…