Han Zhao

Orcid: 0000-0003-1158-0779

Affiliations:
  • University of Illinois at Urbana-Champaign, Department of Computer Science, IL, USA
  • Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh, PA, USA (PhD 2020)
  • University of Waterloo, Department of Computer Science, ON, Canada (former)
  • University of Notre Dame, Department of Computer Science and Engineering, IN, USA (former)


According to our database1, Han Zhao authored at least 97 papers between 2013 and 2024.

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Bibliography

2024
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards.
CoRR, 2024

A survey of recent methods for addressing AI fairness and bias in biomedicine.
CoRR, 2024

Enhancing Compositional Generalization via Compositional Feature Alignment.
CoRR, 2024

Robust Multi-Task Learning with Excess Risks.
CoRR, 2024

2023
Costs and Benefits of Fair Regression.
Trans. Mach. Learn. Res., 2023

Adaptation Augmented Model-based Policy Optimization.
J. Mach. Learn. Res., 2023

Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms.
CoRR, 2023

Gradual Domain Adaptation: Theory and Algorithms.
CoRR, 2023

An Empirical Study of Simplicial Representation Learning with Wasserstein Distance.
CoRR, 2023

Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder.
CoRR, 2023

SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space.
CoRR, 2023

Speciality vs Generality: An Empirical Study on Catastrophic Forgetting in Fine-tuning Foundation Models.
CoRR, 2023

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods.
CoRR, 2023

In-Context Learning of Large Language Models Explained as Kernel Regression.
CoRR, 2023

Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Learning List-Level Domain-Invariant Representations for Ranking.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Learning of Linear Graph Neural Networks via Node Subsampling.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fair and Optimal Classification via Post-Processing.
Proceedings of the International Conference on Machine Learning, 2023

Structural Re-weighting Improves Graph Domain Adaptation.
Proceedings of the International Conference on Machine Learning, 2023

Understanding the Impact of Adversarial Robustness on Accuracy Disparity.
Proceedings of the International Conference on Machine Learning, 2023

Learning Structured Representations by Embedding Class Hierarchy.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

SC<sup>2</sup>GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Adaptive Power Method: Eigenvector Estimation from Sampled Data.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.
IEEE Trans. Neural Networks Learn. Syst., 2022

Algorithms and Theory for Supervised Gradual Domain Adaptation.
Trans. Mach. Learn. Res., 2022

Inherent Tradeoffs in Learning Fair Representations.
J. Mach. Learn. Res., 2022

Fundamental Limits and Tradeoffs in Invariant Representation Learning.
J. Mach. Learn. Res., 2022

Fair and Optimal Classification via Transports to Wasserstein-Barycenter.
CoRR, 2022

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data.
CoRR, 2022

Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning.
CoRR, 2022

Greedy modality selection via approximate submodular maximization.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Provable Domain Generalization via Invariant-Feature Subspace Recovery.
Proceedings of the International Conference on Machine Learning, 2022

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond.
Proceedings of the International Conference on Machine Learning, 2022

Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Conditional Contrastive Learning with Kernel.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Conditional Supervised Contrastive Learning for Fair Text Classification.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Exploring Gradient-Based Multi-directional Controls in GANs.
Proceedings of the Computer Vision - ECCV 2022, 2022

Rethinking Controllable Variational Autoencoders.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Online Continual Adaptation with Active Self-Training.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Return Parity in Markov Decision Processes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Invariant Information Bottleneck for Domain Generalization.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Towards a Unified Framework for Learning and Reasoning.
PhD thesis, 2021

Costs and Benefits of Wasserstein Fair Regression.
CoRR, 2021

Invariant Information Bottleneck for Domain Generalization.
CoRR, 2021

Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations.
CoRR, 2021

Quantifying and Improving Transferability in Domain Generalization.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Machine Learning for Consumers and Markets.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Information Obfuscation of Graph Neural Networks.
Proceedings of the 38th International Conference on Machine Learning, 2021

Understanding and Mitigating Accuracy Disparity in Regression.
Proceedings of the 38th International Conference on Machine Learning, 2021

Self-supervised Representation Learning with Relative Predictive Coding.
Proceedings of the 9th International Conference on Learning Representations, 2021

On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections.
Proceedings of the 9th International Conference on Learning Representations, 2021

EventKE: Event-Enhanced Knowledge Graph Embedding.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Graph Adversarial Networks: Protecting Information against Adversarial Attacks.
CoRR, 2020

DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring.
Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Neural Methods for Point-wise Dependency Estimation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Model-based Policy Optimization with Unsupervised Model Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Learning Language-Invariant Representations for Universal Machine Translation.
Proceedings of the 37th International Conference on Machine Learning, 2020

Continual Learning with Adaptive Weights (CLAW).
Proceedings of the 8th International Conference on Learning Representations, 2020

Conditional Learning of Fair Representations.
Proceedings of the 8th International Conference on Learning Representations, 2020

Deep Fair Clustering for Visual Learning.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Inherent Tradeoffs in Learning Fair Representation.
CoRR, 2019

Adversarial Task-Specific Privacy Preservation under Attribute Attack.
CoRR, 2019

On Learning Invariant Representation for Domain Adaptation.
CoRR, 2019

Efficient Multitask Feature and Relationship Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Neural Networks with Adaptive Regularization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Strategyproof Conference Peer Review.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

On Learning Invariant Representations for Domain Adaptation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations.
Proceedings of the 12th International Conference on Educational Data Mining, 2019

Deep Generative and Discriminative Domain Adaptation.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Adversarial Multiple Source Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multiple Source Domain Adaptation with Adversarial Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Convolutional-Recurrent Neural Networks for Speech Enhancement.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
Discovering Order in Unordered Datasets: Generative Markov Networks.
CoRR, 2017

Multiple Source Domain Adaptation with Adversarial Training of Neural Networks.
CoRR, 2017

Principled Hybrids of Generative and Discriminative Domain Adaptation.
CoRR, 2017

Efficient Multi-task Feature and Relationship Learning.
CoRR, 2017

Efficient Computation of Moments in Sum-Product Networks.
CoRR, 2017

Linear Time Computation of Moments in Sum-Product Networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Online Algorithms for Sum-Product Networks with Continuous Variables.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

A Unified Approach for Learning the Parameters of Sum-Product Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Collapsed Variational Inference for Sum-Product Networks.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Global Network Alignment in the Context of Aging.
IEEE ACM Trans. Comput. Biol. Bioinform., 2015

Self-Adaptive Hierarchical Sentence Model.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

On the Relationship between Sum-Product Networks and Bayesian Networks.
Proceedings of the 32nd International Conference on Machine Learning, 2015

SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
A Sober Look at Spectral Learning.
CoRR, 2014

2013
Using Global Network Alignment In The Context Of Aging.
Proceedings of the ACM Conference on Bioinformatics, 2013


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