5 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
Test Time Training for Supervised Causal Learning
Zizhen Deng, Jiaru Zhang, Rui Ding +5
Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution gene…
Test-Time Learning of Causal Structure from Interventional Data
Wei Chen, Rui Ding, Bojun Huang +5
Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention…
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
Jiaru Zhang, Rui Ding, Qiang Fu +6
Causal discovery is a structured prediction task that aims to predict causal relations among variables based on their data samples. Supervised Causal Learning (SCL) is an emerging…
Utility-Probability Duality of Neural Networks
Huang Bojun, Fei Yuan
It is typically understood that the training of modern neural networks is a process of fitting the probability distribution of desired output. However, recent paradoxical observati…
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
Huang Bojun
This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear…
Steady State Analysis of Episodic Reinforcement Learning
Huang Bojun
This paper proves that the episodic learning environment of every finite-horizon decision task has a unique steady state under any behavior policy, and that the marginal distributi…