Yang Liu

Yang Liu

University of California, Santa Cruz

H-index: 31

North America-United States

About Yang Liu

Yang Liu, With an exceptional h-index of 31 and a recent h-index of 31 (since 2020), a distinguished researcher at University of California, Santa Cruz, specializes in the field of weakly supervised learning, algorithmic fairness, machine learning, incentive and consensus.

His recent articles reflect a diverse array of research interests and contributions to the field:

Fair participation via sequential policies

Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework

Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards

Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation

Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees

Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting

Rethinking Machine Unlearning for Large Language Models

Long-term fairness with unknown dynamics

Yang Liu Information

University

Position

Computer Science and Engineering

Citations(all)

3956

Citations(since 2020)

3680

Cited By

966

hIndex(all)

31

hIndex(since 2020)

31

i10Index(all)

64

i10Index(since 2020)

60

Email

University Profile Page

Google Scholar

Yang Liu Skills & Research Interests

weakly supervised learning

algorithmic fairness

machine learning

incentive and consensus

Top articles of Yang Liu

Fair participation via sequential policies

Proceedings of the AAAI Conference on Artificial Intelligence

2024/3/24

Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework

arXiv preprint arXiv:2403.08743

2024/3/13

Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards

arXiv preprint arXiv:2401.16635

2024/1/30

Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation

arXiv preprint arXiv:2403.05171

2024/3/8

Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees

arXiv preprint arXiv:2402.17106

2024/2/27

Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting

arXiv preprint arXiv:2402.10412

2024/2/16

Long-term fairness with unknown dynamics

Advances in Neural Information Processing Systems

2024/2/13

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

arXiv preprint arXiv:2402.02032

2024/2/3

Human-instruction-free llm self-alignment with limited samples

arXiv preprint arXiv:2401.06785

2024/1/6

The importance of human-labeled data in the era of LLMs

arXiv preprint arXiv:2306.14910

2023/6/18

Fair Learning to Rank with Distribution-free Risk Control

arXiv preprint arXiv:2306.07188

2023/6/12

T2iat: Measuring valence and stereotypical biases in text-to-image generation

arXiv preprint arXiv:2306.00905

2023/6/1

Do humans and machines have the same eyes? human-machine perceptual differences on image classification

arXiv preprint arXiv:2304.08733

2023/4/18

Model sparsification can simplify machine unlearning

arXiv preprint arXiv:2304.04934

2023/4/11

Fairness improves learning from noisily labeled long-tailed data

arXiv preprint arXiv:2303.12291

2023/3/22

Uncertainty-Aware Off-Policy Learning

arXiv preprint arXiv:2303.06389

2023/3/11

Machine truth serum: a surprisingly popular approach to improving ensemble methods

Machine Learning

2023/3

Surrogate scoring rules

ACM Transactions on Economics and Computation

2023/2/15

Tier balancing: Towards dynamic fairness over underlying causal factors

2023/1/21

See List of Professors in Yang Liu University(University of California, Santa Cruz)

Co-Authors

academic-engine