22 citations · 48 across the 15 of their papers we have counts for
9 papers · 1 filter
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
Yujie Lin, Chen Zhao, Minglai Shao +3
Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance o…
Adaptation Speed Analysis for Fairness-aware Causal Models
Yujie Lin, Chen Zhao, Minglai Shao +2
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the trai…
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
Dong Li, Wenjun Wang, Minglai Shao +1
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-ce…
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Yanda Chen, Ruiqi Zhong, Narutatsu Ri +5
Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs pro…
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Yilun Zhao, Chen Zhao, Linyong Nan +5
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task…
Getting MoRE out of Mixture of Language Model Reasoning Experts
Chenglei Si, Weijia Shi, Chen Zhao +2
While recent large language models (LLMs) improve on various question answering (QA) datasets, it remains difficult for a single model to generalize across question types that requ…
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Angelica Chen, Jason Phang, Alicia Parrish +4
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consist…
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation
Chen Zhao, Wei-Ling Cai, Zheng Yuan +1
Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the ne…
Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation
Chen Zhao, Wei-Ling Cai, Zheng Yuan
Existing image-to-image (I2I) translation methods achieve state-of-the-art performance by incorporating the patch-wise contrastive learning into Generative Adversarial Networks. Ho…