5 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
Simpson's Bias in NLP Training
Fei Yuan, Longtu Zhang, Huang Bojun +1
In most machine learning tasks, we evaluate a model on a given data population by measuring a population-level metric . Examples of such evaluation metric inclu…
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…
Sequential Resource Allocation with Positional Costs
Bojun Huang
We consider the problem of minimizing the total cost to run a sequence of tasks in the given order by agents under the positional cost model. The cost to run a task not onl…