20 citations · 59 across the 35 of their papers we have counts for
13 papers · 1 filter
Enforcing Constraints in Generative Sampling via Adaptive Correction Scheduling
Noah Trupin, Yexiang Xue
Hard constraints in generative sampling are typically enforced by projection, applied either once at the end of sampling or after every update. This binary framing overlooks a fund…
Zero-shot Imitation Learning by Latent Topology Mapping
Maxwell J. Jacobson, Yexiang Xue
Imitation learning is effective for training agents when expert demonstrations are available, but collecting demonstrations for every complex task in an environment is costly. We s…
Vertical Symbolic Regression via Deep Policy Gradient
Nan Jiang, Md Nasim, Yexiang Xue
Vertical Symbolic Regression (VSR) recently has been proposed to expedite the discovery of symbolic equations with many independent variables from experimental data. VSR reduces th…
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains
Md Nasim, Yexiang Xue
Accelerating the learning of Partial Differential Equations (PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit spa…
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper proposes an algorithm that aims to improve generalization for reinforcement learning agents by removing overfitting to confounding features. Our approach consists of a m…
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…
Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
The policy gradient method enjoys the simplicity of the objective where the agent optimizes the cumulative reward directly. Moreover, in the continuous action domain, parameterized…
Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma
Nan Jiang, Yi Gu, Yexiang Xue
Learning to generate complex combinatorial structures satisfying constraints will have transformative impacts in many application domains. However, it is beyond the capabilities of…
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…
Towards Efficient Discrete Integration via Adaptive Quantile Queries
Fan Ding, Hanjing Wang, Ashish Sabharwal +1
Discrete integration in a high dimensional space of n variables poses fundamental challenges. The WISH algorithm reduces the intractable discrete integration problem into n optimiz…
End-to-End Learning for the Deep Multivariate Probit Model
Di Chen, Yexiang Xue, Carla P. Gomes
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP mod…
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Luming Tang, Yexiang Xue, Di Chen +1
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme…
Deep Multi-Species Embedding
Di Chen, Yexiang Xue, Shuo Chen +2
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only targe…