Search Results for author: Haoliang Wang

Found 10 papers, 4 papers with code

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

no code implementations14 Mar 2024 Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, YuHang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.

Causal Inference Fairness

VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding

no code implementations4 Dec 2023 Yizhou Wang, Ruiyi Zhang, Haoliang Wang, Uttaran Bhattacharya, Yun Fu, Gang Wu

Recent advancements in language-model-based video understanding have been progressing at a remarkable pace, spurred by the introduction of Large Language Models (LLMs).

Language Modelling Question Answering +2

Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously

1 code implementation23 Nov 2023 Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen

The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricate challenge in machine learning.

Domain Generalization Fairness +1

Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective

no code implementations18 Sep 2023 Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen

In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.

Domain Generalization

Measuring and Modeling Physical Intrinsic Motivation

no code implementations22 May 2023 Julio Martinez, Felix Binder, Haoliang Wang, Nick Haber, Judith Fan, Daniel L. K. Yamins

Finally, linearly combining the adversarial model with the number of collisions in a scene leads to the greatest improvement in predictivity of human responses, suggesting humans are driven towards scenarios that result in high information gain and physical activity.

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning

no code implementations20 May 2023 Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl

In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.

Dialogue State Tracking Transfer Learning

Layer Adaptive Deep Neural Networks for Out-of-distribution Detection

1 code implementation1 Mar 2022 Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen

During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Automatic Forecasting via Meta-Learning

no code implementations29 Sep 2021 Mustafa Abdallah, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Handong Zhao, Haoliang Wang, Saurabh Bagchi

In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.

Meta-Learning Time Series +1

Learning to communicate about shared procedural abstractions

1 code implementation30 Jun 2021 William P. McCarthy, Robert D. Hawkins, Haoliang Wang, Cameron Holdaway, Judith E. Fan

Many real-world tasks require agents to coordinate their behavior to achieve shared goals.

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