Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders
- Andrew Geng
- Pin-Yu Chen
- 2024
- IEEE SaTML 2024
Pin-Yu Chen
Title
Principal Research Scientist; Chief Scientist, RPI-IBM AI Research Collaboration
Bio
Pin-Yu Chen is a Principal Research Staff Member of the Trusted AI Group, IBM Thomas J. Watson Research Center. He is also the Chief Scientist of RPI-IBM AI Research Collaboration program and a PI of MIT-IBM Watson AI Lab projects.
His recent research focuses on adversarial machine learning of neural networks for robustness and safety, and more broadly, making machine learning trustworthy.
♦ Pin-Yu's research highlights can be found from HERE
♦ Pin-Yu is open to collaboration with highly motivated researchers!
Pin-Yu received his Ph.D. degree in electrical engineering and computer science (EECS) and M.A. degree in Statistics from the University of Michigan Ann Arbor in 2016, under the supervision of Prof. Alfred Hero.
♦ Please refer to his personal webpage for more details - www.pinyuchen.com
Publications
Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning
- 2024
- AAAI 2024
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
- Shengwei An
- Sheng-yen Chou
- et al.
- 2024
- AAAI 2024
Zeroth-Order Machine Learning: Fundamental Principles and Emerging Applications in Foundation Models
- Sijia Liu
- Tianlong Chen
- et al.
- 2024
- AAAI 2024
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
- Sheng-yen Cho
- Pin-Yu Chen
- et al.
- 2023
- NeurIPS 2023
RADAR: Robust AI-Text Detection via Adversarial Learning
- Xiaomeng Hu
- Pin-Yu Chen
- et al.
- 2023
- NeurIPS 2023
Uncovering and Quantifying Social Biases in Code Generation
- Yan Liu
- Xiaokang Chen
- et al.
- 2023
- NeurIPS 2023
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
- Chen Chen
- Yuchen Hu
- et al.
- 2023
- NeurIPS 2023
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with epsilon-Greedy Exploration
- Shuai Zhang
- Hongkang Li
- et al.
- 2023
- NeurIPS 2023
Parameter Efficient Finetuning for Reducing Activation Density in Transformers
- Bharat Runwal
- Tejaswini Pedapati
- et al.
- 2023
- NeurIPS 2023
Patents
- US
- 11836256
- CN
- ZL201910306083.X
- CN
- ZL202010020033.8
- JP
- 7316722
- US
- 11640532
- US
- 11625487
- US
- 11604994
- US
- 11586912
- US
- 11568282
- US
- 11568267
Blog posts
Serving customized AI models at scale with LoRA
ResearchKim MartineauWhat’s an LLM context window and why is it getting larger?
NewsKim MartineauWhat is red teaming for generative AI?
ExplainerKim MartineauAccelerating molecular optimization with AI
Deep DivePayel Das, Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, and Pin-Yu Chen11 minute read- See more blog posts
Top collaborators
PD
Payel Das
Principal Research Scientist and Manager, IBM Research AI
KV
Kush Varshney
IBM Fellow
MP
Mark Purcell
Research Manager - Security Research, AI Security & PETs
AR
Ambrish Rawat
Senior Research Scientist, AI Security and Privacy, Master Inventor