Po-Yi Lu (呂栢頤)

I am a fourth-year Ph. D. student at the Graduate Institute of Networking and Multimedia at National Taiwan University. I’m also the Associate Learner in CLLab, advised by Prof. Hsuan-Tien Lin. Previously, I was a researcher at Chunghwa Telecom for 5 years.

My research interests are preference alignment (AI safety), active learning (human-in-the-loop), and weakly-supervised learning (limited-label learning). I also enjoyed promoting human thinking about AI safety and Ethics, specifically, I organized the AI Safety Taiwan Group sponsored by the Effective Altruism (EA) community.

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News

  • [June 2025] Our paper about re‐benchmarking active learning has been accepted by TMLR 2025.
  • [May 2025] Our paper about universal domain adaptation has been accepted by ICML 2025.
  • [February 2025] Our paper about AI labeling for complementary label learning has been accepted by PAKDD 2025.
  • [January 2025] I will serve as an Assistant Program Chair for NeurIPS 2025.

Research

Conference Publications

Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation

Hung-Chieh Fang, Po-Yi Lu, Hsuan-Tien Lin
In Proceedings of the 42nd International Conference on Machine Learning 2025

We investigate the self-supervised loss to tackle dimensional collapse in the target representation and step toward more comprehensive Universal Domain Adaptation.

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The Unexplored Potential of Vision-Language Models for Generating Large-Scale Complementary-Label Learning Data

Tan-Ha Mai, Nai-Xuan Ye, Yu-Wei Kuan, Po-Yi Lu, and Hsuan-Tien Lin
In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2025.

We make Complementary Label Learning practical with Vision-Language Model-based auto-labeling for large-scale data.

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Defending Text-to-image Diffusion Models: Surprising Efficacy of Textual Perturbations Against Backdoor Attacks

Oscar Chew, Po-Yi Lu, Jayden Lin, Hsuan-Tien Lin
In the Dark Side of Generative AIs and Beyond Workshop @ ECCV, 2024.

We propose straightforward textual perturbations on prompts to defend against backdoor attacks for text-to-image tasks.

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A More Robust Baseline for Active Learning by Injecting Randomness to Uncertainty Sampling

Po-Yi Lu, Chun-Liang Li, and Hsuan-Tien Lin
In the Artificial Intelligence & Human Computer Interaction @ ICML 2023.

We investigate the injecting of slight randomness into uncertainty sampling to balance the bias in the pure uncertainty sampling with a small variance.

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Journal Publications

An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets

Po-Yi Lu, Yi-Jie Cheng, Chun-Liang Li, and Hsuan-Tien Lin
Transactions on Machine Learning Research (TMLR), 2025. Reproducibility Certification

We re-benchmark active learning in tabular datasets and affirm the effectiveness of uncertainty sampling.

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Experience

Education

  • September 2013–June 2015, M.S. @ Institute of Industrial Engineering, National Taiwan University
  • September 2009–June 2013, B.C. @ Department of Mathematical Science, National ChengChi University

Work

  • May 2018–May 2021, Researcher @ Telecommunication Laboratories, Chunghwa Telecom
  • December 2015–April 2018, Associate Researcher @ Telecommunication Laboratories, Chunghwa Telecom

Services

Academic Activities

  • Assistant Program Chair: NeurIPS 2025
  • Session chair: ACML 2024
  • Reviewer: AISTATS 2024, ACML 2024, AISTATS 2025, NeurIPS D&B Track 2025

Teaching

  • September 2020–December 2023, Instructor @ Information Club, Chingshin Academy
  • Fall 2023, Machine Learning, (as Teaching Assistant)
  • Spring 2023, Machine Learning, (as Head of Teaching Assistant of 12 TAs)
  • Spring 2015, Linear Algebra and Its Applications (IE5034)
  • Fall 2014, Introduction to Statistical Control and Optimization (IE5049)

Community

Awards

  • 2020 T-Brain E.SUN NLP AML detection competition: Rank 19/409 (5%)
  • 2017 Taiwan Innotech Expo, Gold Medal Award
  • 2015 Big Data Analytics for Semiconductor Manufacturing by TSMC, Honourable Mention Award

This template is adapted from Jon Barron.