Brian R. Bartoldson

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 15

Top Topics

Labeling Problems

Pruning Algorithm

Top Venues

J. Mach. Learn. Res.

Publications

Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. CoRR (2024)
Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
Transformers Can Do Arithmetic with the Right Embeddings. CoRR (2024)
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR (2024)
Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. CoRR (2024)
Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash
ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling. CoRR (2024)
Brian R. Bartoldson, Yeping Hu, Amar Saini, Jose Cadena, Yucheng Fu, Jie Bao, Zhijie Xu, Brenda Ng, Phan Nguyen
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics. CoRR (2023)
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. J. Mach. Learn. Res. 24 (2023)
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR (2023)
Brian R. Bartoldson, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng
Latent Space Simulation for Carbon Capture Design Optimization. AAAI (2022)
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. NeurIPS (2022)
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. CoRR (2022)
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. CoRR (2022)
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. CoRR (2021)
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. NeurIPS (2021)
Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher
The Generalization-Stability Tradeoff in Neural Network Pruning. CoRR (2019)