b'Han Zhao'

research

∙ 05/22/2023

In-Context Learning of Large Language Models Explained as Kernel Regression

Large language models (LLMs) have initiated a paradigm shift in transfer...

0 Chi Han, et al. ∙

research

∙ 04/20/2023

Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs

How can we learn effective node representations on textual graphs? Graph...

0 Costas Mavromatis, et al. ∙

research

∙ 03/10/2023

Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning

Contrastive loss has been increasingly used in learning representations ...

0 Qian Jiang, et al. ∙

research

∙ 01/27/2023

Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images

Despite recent advances in data-independent and deep-learning algorithms...

0 Fei Pan, et al. ∙

research

∙ 12/21/2022

Learning List-Level Domain-Invariant Representations for Ranking

Domain adaptation aims to transfer the knowledge acquired by models trai...

0 Ruicheng Xian, et al. ∙

research

∙ 11/03/2022

Fair and Optimal Classification via Transports to Wasserstein-Barycenter

Fairness in automated decision-making systems has gained increasing atte...

0 Ruicheng Xian, et al. ∙

research

∙ 10/22/2022

Greedy Modality Selection via Approximate Submodular Maximization

Multimodal learning considers learning from multi-modality data, aiming ...

0 Runxiang Cheng, et al. ∙

research

∙ 09/27/2022

Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery

Accurate vehicle type classification serves a significant role in the in...

0 Ruikang Luo, et al. ∙

research

∙ 09/14/2022

Interpolation-based immersed finite element and isogeometric analysis

We introduce a new paradigm for immersed finite element and isogeometric...

0 Jennifer E. Fromm, et al. ∙

research

∙ 09/01/2022

Exploring Gradient-based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in model...

12 Zikun Chen, et al. ∙

research

∙ 08/31/2022

Application of Data Encryption in Chinese Named Entity Recognition

Recently, with the continuous development of deep learning, the performa...

4 Kaifang Long, et al. ∙

research

∙ 07/21/2022

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

Federated learning (FL) provides an effective paradigm to train machine ...

2 Wenda Chu, et al. ∙

research

∙ 05/23/2022

Conditional Supervised Contrastive Learning for Fair Text Classification

Contrastive representation learning has gained much attention due to its...

0 Jianfeng Chi, et al. ∙

research

∙ 04/25/2022

Algorithms and Theory for Supervised Gradual Domain Adaptation

The phenomenon of data distribution evolving over time has been observed...

0 Jing Dong, et al. ∙

research

∙ 04/18/2022

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond

The vast majority of existing algorithms for unsupervised domain adaptat...

0 Haoxiang Wang, et al. ∙

research

∙ 03/09/2022

Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning

Despite achieving state-of-the-art zero-shot performance, existing visio...

0 Zhenhailong Wang, et al. ∙

research

∙ 02/11/2022

Conditional Contrastive Learning with Kernel

Conditional contrastive learning frameworks consider the conditional sam...

10 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 01/30/2022

Provable Domain Generalization via Invariant-Feature Subspace Recovery

Domain generalization asks for models trained on a set of training envir...

0 Haoxiang Wang, et al. ∙

research

∙ 11/19/2021

Towards Return Parity in Markov Decision Processes

Algorithmic decisions made by machine learning models in high-stakes dom...

0 Jianfeng Chi, et al. ∙

research

∙ 06/16/2021

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation

Multi-task learning (MTL) aims to improve the generalization of several ...

0 Haoxiang Wang, et al. ∙

research

∙ 06/16/2021

Costs and Benefits of Wasserstein Fair Regression

Real-world applications of machine learning tools in high-stakes domains...

0 Han Zhao, et al. ∙

research

∙ 06/11/2021

Online Continual Adaptation with Active Self-Training

Models trained with offline data often suffer from continual distributio...

0 Shiji Zhou, et al. ∙

research

∙ 06/11/2021

Invariant Information Bottleneck for Domain Generalization

The main challenge for domain generalization (DG) is to overcome the pot...

0 Bo Li, et al. ∙

research

∙ 06/07/2021

Quantifying and Improving Transferability in Domain Generalization

Out-of-distribution generalization is one of the key challenges when tra...

0 Guojun Zhang, et al. ∙

research

∙ 06/05/2021

Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations

Self-supervised learning is a form of unsupervised learning that leverag...

44 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 03/21/2021

Self-supervised Representation Learning with Relative Predictive Coding

This paper introduces Relative Predictive Coding (RPC), a new contrastiv...

32 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/24/2021

Understanding and Mitigating Accuracy Disparity in Regression

With the widespread deployment of large-scale prediction systems in high...

0 Jianfeng Chi, et al. ∙

research

∙ 01/29/2021

Acoustic Structure Inverse Design and Optimization Using Deep Learning

From ancient to modern times, acoustic structures have been used to cont...

16 Xuecong Sun, et al. ∙

research

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

0 Han Zhao, et al. ∙

research

∙ 10/19/2020

Model-based Policy Optimization with Unsupervised Model Adaptation

Model-based reinforcement learning methods learn a dynamics model with r...

0 Jian Shen, et al. ∙

research

∙ 10/09/2020

Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation

The success of supervised learning hinges on the assumption that the tra...

5 Bo Li, et al. ∙

research

∙ 09/28/2020

Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph ...

20 Peiyuan Liao, et al. ∙

research

∙ 09/01/2020

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

Large-scale labeled training datasets have enabled deep neural networks ...

7 Sicheng Zhao, et al. ∙

research

∙ 08/11/2020

On Learning Language-Invariant Representations for Universal Machine Translation

The goal of universal machine translation is to learn to translate betwe...

38 Han Zhao, et al. ∙

research

∙ 06/09/2020

Neural Methods for Point-wise Dependency Estimation

Since its inception, the neural estimation of mutual information (MI) ha...

13 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 03/10/2020

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

Adversarial learning has demonstrated good performance in the unsupervis...

18 Remi Tachet des Combes, et al. ∙

research

∙ 11/21/2019

Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of rela...

20 Tameem Adel, et al. ∙

research

∙ 10/16/2019

Conditional Learning of Fair Representations

We propose a novel algorithm for learning fair representations that can ...

0 Han Zhao, et al. ∙

research

∙ 07/14/2019

Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an in...

3 Han Zhao, et al. ∙

research

∙ 06/19/2019

Inherent Tradeoffs in Learning Fair Representation

With the prevalence of machine learning in high-stakes applications, esp...

0 Han Zhao, et al. ∙

research

∙ 06/19/2019

Adversarial Task-Specific Privacy Preservation under Attribute Attack

With the prevalence of machine learning services, crowdsourced data cont...

0 Han Zhao, et al. ∙

research

∙ 01/27/2019

On Learning Invariant Representation for Domain Adaptation

Due to the ability of deep neural nets to learn rich representations, re...

0 Han Zhao, et al. ∙

research

∙ 06/16/2018

On Strategyproof Conference Peer Review

We consider peer review in a conference setting where there is typically...

0 Yichong Xu, et al. ∙

research

∙ 05/02/2018

Convolutional-Recurrent Neural Networks for Speech Enhancement

We propose an end-to-end model based on convolutional and recurrent neur...

0 Han Zhao, et al. ∙

research

∙ 01/19/2018

Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations

Strict partial order is a mathematical structure commonly seen in relati...

0 Chen Liang, et al. ∙

research

∙ 06/20/2017

Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint

We propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegati...

0 Han Zhao, et al. ∙

research

∙ 05/26/2017

Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

While domain adaptation has been actively researched in recent years, mo...

0 Han Zhao, et al. ∙

research

∙ 05/25/2017

Principled Hybrids of Generative and Discriminative Domain Adaptation

We propose a probabilistic framework for domain adaptation that blends b...

0 Han Zhao, et al. ∙

research

∙ 02/15/2017

Linear Time Computation of Moments in Sum-Product Networks

Bayesian online algorithms for Sum-Product Networks (SPNs) need to updat...

0 Han Zhao, et al. ∙

research

∙ 02/14/2017

Efficient Multi-task Feature and Relationship Learning

In this paper we propose a multi-convex framework for multi-task learnin...

0 Han Zhao, et al. ∙

Han Zhao

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