b'Kun Kuang'

research

∙ 09/21/2023

A Chinese Prompt Attack Dataset for LLMs with Evil Content

Large Language Models (LLMs) present significant priority in text unders...

0 Chengyuan Liu, et al. ∙

research

∙ 08/16/2023

Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series

Learning directed acyclic graphs (DAGs) to identify causal relations und...

0 Anpeng Wu, et al. ∙

research

∙ 05/25/2023

Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization

Domain generalization (DG) is a prevalent problem in real-world applicat...

0 Yunze Tong, et al. ∙

research

∙ 04/28/2023

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction

Universal Information Extraction (UIE) is an area of interest due to the...

0 Chengyuan Liu, et al. ∙

research

∙ 04/24/2023

Universal Domain Adaptation via Compressive Attention Matching

Universal domain adaptation (UniDA) aims to transfer knowledge from the ...

0 Didi Zhu, et al. ∙

research

∙ 02/14/2023

IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System

Recommendation systems have shown great potential to solve the informati...

0 Zheqi Lv, et al. ∙

research

∙ 02/14/2023

Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning

Real-world cooperation often requires intensive coordination among agent...

0 Shanqi Liu, et al. ∙

research

∙ 12/12/2022

Instrumental Variables in Causal Inference and Machine Learning: A Survey

Causal inference is the process of using assumptions, study designs, and...

0 Anpeng Wu, et al. ∙

research

∙ 12/04/2022

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

The success of deep learning is partly attributed to the availability of...

0 Qi Tian, et al. ∙

research

∙ 11/28/2022

Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning

Offline multi-agent reinforcement learning (MARL) aims to learn effectiv...

0 Qi Tian, et al. ∙

research

∙ 11/18/2022

Confounder Balancing for Instrumental Variable Regression with Latent Variable

This paper studies the confounding effects from the unmeasured confounde...

0 Anpeng Wu, et al. ∙

research

∙ 11/15/2022

Exploiting Contrastive Learning and Numerical Evidence for Improving Confusing Legal Judgment Prediction

Given the fact description text of a legal case, legal judgment predicti...

0 Leilei Gan, et al. ∙

research

∙ 10/25/2022

Learning Individual Treatment Effects under Heterogeneous Interference in Networks

Estimates of individual treatment effects from networked observational d...

0 Ziyu Zhao, et al. ∙

research

∙ 10/16/2022

Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples

The aim of Logic2Text is to generate controllable and faithful texts con...

0 Chengyuan Liu, et al. ∙

research

∙ 10/06/2022

Domain Generalization via Contrastive Causal Learning

Domain Generalization (DG) aims to learn a model that can generalize wel...

0 Qiaowei Miao, et al. ∙

research

∙ 09/12/2022

DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization

Device Model Generalization (DMG) is a practical yet under-investigated ...

12 Zheqi Lv, et al. ∙

research

∙ 08/23/2022

Treatment Effect Estimation with Unmeasured Confounders in Data Fusion

In the presence of unmeasured confounders, we address the problem of tre...

0 Anpeng Wu, et al. ∙

research

∙ 08/19/2022

Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

In an era of information explosion, recommendation systems play an impor...

0 Zheqi Lv, et al. ∙

research

∙ 08/07/2022

Label-Efficient Domain Generalization via Collaborative Exploration and Generalization

Considerable progress has been made in domain generalization (DG) which ...

0 Junkun Yuan, et al. ∙

research

∙ 06/20/2022

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

Collaborative multi-agent reinforcement learning (MARL) has been widely ...

0 Shuang Luo, et al. ∙

research

∙ 06/07/2022

Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning

While annotating decent amounts of data to satisfy sophisticated learnin...

0 Jiannan Guo, et al. ∙

research

∙ 01/19/2022

Debiased Graph Neural Networks with Agnostic Label Selection Bias

Most existing Graph Neural Networks (GNNs) are proposed without consider...

0 Shaohua Fan, et al. ∙

research

∙ 12/20/2021

Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth

Recent studies have shown that introducing communication between agents ...

0 Qi Tian, et al. ∙

research

∙ 11/11/2021

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey

Influenced by the great success of deep learning via cloud computing and...

0 Jiangchao Yao, et al. ∙

research

∙ 11/09/2021

Unified Group Fairness on Federated Learning

Federated learning (FL) has emerged as an important machine learning par...

0 Fengda Zhang, et al. ∙

research

∙ 11/05/2021

Dialogue Inspectional Summarization with Factual Inconsistency Awareness

Dialogue summarization has been extensively studied and applied, where t...

0 Leilei Gan, et al. ∙

research

∙ 10/13/2021

Do We Need to Directly Access the Source Datasets for Domain Generalization?

Domain generalization (DG) aims to learn a generalizable model from mult...

0 Junkun Yuan, et al. ∙

research

∙ 10/08/2021

Stable Prediction on Graphs with Agnostic Distribution Shift

Graph is a flexible and effective tool to represent complex structures i...

0 Shengyu Zhang, et al. ∙

research

∙ 10/04/2021

Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization

Domain generalization (DG) aims to learn from multiple source domains a ...

0 Junkun Yuan, et al. ∙

research

∙ 10/02/2021

Domain-Specific Bias Filtering for Single Labeled Domain Generalization

Domain generalization (DG) utilizes multiple labeled source datasets to ...

0 Junkun Yuan, et al. ∙

research

∙ 07/13/2021

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition

Instrumental variables (IVs), sources of treatment randomization that ar...

0 Junkun Yuan, et al. ∙

research

∙ 06/30/2021

Distributionally Robust Learning with Stable Adversarial Training

Machine learning algorithms with empirical risk minimization are vulnera...

0 Jiashuo Liu, et al. ∙

research

∙ 06/01/2021

Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning

Centralized Training with Decentralized Execution (CTDE) has been a popu...

0 Jiahui Li, et al. ∙

research

∙ 05/29/2021

Analysis and Applications of Class-wise Robustness in Adversarial Training

Adversarial training is one of the most effective approaches to improve ...

0 Qi Tian, et al. ∙

research

∙ 05/17/2021

Dependency Parsing as MRC-based Span-Span Prediction

Higher-order methods for dependency parsing can partially but not fully ...

0 Leilei Gan, et al. ∙

research

∙ 05/12/2021

BertGCN: Transductive Text Classification by Combining GCN and BERT

In this work, we propose BertGCN, a model that combines large scale pret...

0 Yuxiao Lin, et al. ∙

research

∙ 11/07/2020

Interventional Domain Adaptation

Domain adaptation (DA) aims to transfer discriminative features learned ...

0 Jun Wen, et al. ∙

research

∙ 10/18/2020

Federated Unsupervised Representation Learning

To leverage enormous unlabeled data on distributed edge devices, we form...

0 Fengda Zhang, et al. ∙

research

∙ 08/16/2020

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

In this paper, we propose to investigate the problem of out-of-domain vi...

0 Shengyu Zhang, et al. ∙

research

∙ 08/16/2020

Poet: Product-oriented Video Captioner for E-commerce

In e-commerce, a growing number of user-generated videos are used for pr...

0 Shengyu Zhang, et al. ∙

research

∙ 08/13/2020

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many industri...

0 Yufei Feng, et al. ∙

research

∙ 06/29/2020

Decorrelated Clustering with Data Selection Bias

Most of existing clustering algorithms are proposed without considering ...

0 Xiao Wang, et al. ∙

research

∙ 06/24/2020

Comprehensive Information Integration Modeling Framework for Video Titling

In e-commerce, consumer-generated videos, which in general deliver consu...

0 Shengyu Zhang, et al. ∙

research

∙ 06/18/2020

Algorithmic Decision Making with Conditional Fairness

Nowadays fairness issues have raised great concerns in decision-making s...

0 Renzhe Xu, et al. ∙

research

∙ 06/12/2020

Learning Decomposed Representation for Counterfactual Inference

One fundamental problem in the learning treatment effect from observatio...

0 Anpeng Wu, et al. ∙

research

∙ 06/09/2020

Stable Prediction via Leveraging Seed Variable

In this paper, we focus on the problem of stable prediction across unkno...

28 Kun Kuang, et al. ∙

research

∙ 06/08/2020

Invariant Adversarial Learning for Distributional Robustness

Machine learning algorithms with empirical risk minimization are vulnera...

0 Jiashuo Liu, et al. ∙

research

∙ 06/08/2020

Balance-Subsampled Stable Prediction

In machine learning, it is commonly assumed that training and test data ...

0 Kun Kuang, et al. ∙

research

∙ 01/31/2020

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required ...

95 Kun Kuang, et al. ∙

research

∙ 06/16/2018

Stable Prediction across Unknown Environments

In many important machine learning applications, the training distributi...

14 Kun Kuang, et al. ∙

Kun Kuang

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