b'Han Zhao'

research

∙ 04/25/2022

Algorithms and Theory for Supervised Gradual Domain Adaptation

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

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...

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...

Zhenhailong Wang, et al. ∙

research

∙ 02/11/2022

Conditional Contrastive Learning with Kernel

Conditional contrastive learning frameworks consider the conditional sam...

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...

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...

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 ...

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...

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...

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...

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...

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...

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...

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...

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...

Xuecong Sun, et al. ∙

research

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

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...

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...

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 ...

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 ...

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...

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...

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...

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...

Tameem Adel, et al. ∙

research

∙ 10/16/2019

Conditional Learning of Fair Representations

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

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

Han Zhao, et al. ∙

research

∙ 01/03/2016

A Unified Approach for Learning the Parameters of Sum-Product Networks

We present a unified approach for learning the parameters of Sum-Product...

Han Zhao, et al. ∙

research

∙ 04/20/2015

Self-Adaptive Hierarchical Sentence Model

The ability to accurately model a sentence at varying stages (e.g., word...

Han Zhao, et al. ∙

research

∙ 01/06/2015

On the Relationship between Sum-Product Networks and Bayesian Networks

In this paper, we establish some theoretical connections between Sum-Pro...

Han Zhao, et al. ∙

Han Zhao

Featured Co-authors