24 citations · 38 across the 12 of their papers we have counts for
7 papers · 1 filter
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning
Tingrui Huang, Ai Bo, Junzhe Zhang
Contrastive reinforcement learning (CRL) learns goal-conditioned values by associating state--action pairs with the states subsequently reached, assuming that no unobserved variabl…
An Introduction to Causal Reinforcement Learning
Elias Bareinboim, Junzhe Zhang, Sanghack Lee
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.e., w…
Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding
Junzhe Zhang, Jingyuan Chen, Elias Bareinboim
The presence of confounding bias poses a key challenge in policy evaluation, as the target causal effects of actions are not identifiable (i.e., underdetermined) from observational…
Constrained Optimization Algorithms for Orbital Optimization in Quantum Chemistry
Junzhe Zhang, Shuoyi Hu, Bing Gu
We present a modular constrained-orbital-optimization framework for quantum chemistry. The formulation separates the correlated electronic-structure solver from the orbital optimiz…
Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images
Jingyuan Chen, Kangrui Ruan, Junzhe Zhang
Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains. A common source is an unobserved confounde…
Lagrangian Flow Matching: A Least-Action Framework for Principled Path Design
Shukai Du, Junzhe Zhang, Yiming Li
Flow matching trains a neural velocity field by regression against a target velocity associated with a prescribed probability path connecting a simple initial distribution to the d…
Causal Flow Q-Learning for Robust Offline Reinforcement Learning
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
Expressive policies based on flow-matching have been successfully applied in reinforcement learning (RL) more recently due to their ability to model complex action distributions fr…