Brian R. Bartoldson

39 papers A* 10Journal 28Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multi-Token Prediction via Self-Distillation. CoRR John Kirchenbauer, Abhimanyu Hans, Brian R. Bartoldson, Micah Goldblum, Ashwinee Panda, Tom Goldstein
2026	A*	`conf`	STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. AAAI Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Yanqing Liu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie
2025	J	`jnl`	A Comedy of Estimators: On KL Regularization in RL Training of LLMs. CoRR Vedant Shah, Johan S. Obando-Ceron, Vineet Jain, Brian R. Bartoldson, Bhavya Kailkhura, Sarthak Mittal, Glen Berseth, Pablo Samuel Castro, Yoshua Bengio, Nikolay Malkin, Moksh Jain, Siddarth Venkatraman, Aaron C. Courville
2025	J	`jnl`	Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts. CoRR Haizhong Zheng, Yang Zhou, Brian R. Bartoldson, Bhavya Kailkhura, Fan Lai, Jiawei Zhao, Beidi Chen
2025	J	`jnl`	AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security. CoRR Zikui Cai, Shayan Shabihi, Bang An, Zora Che, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein, Furong Huang
2025	J	`jnl`	Constrained Language Generation with Discrete Diffusion Models. CoRR Michael Cardei, Jacob K. Christopher, Thomas Hartvigsen, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto
2025	J	`jnl`	Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness. CoRR Zeyu Wang, Cihang Xie, Brian R. Bartoldson, Bhavya Kailkhura
2025	A*	`conf`	ELFS: Label-Free Coreset Selection with Proxy Training Dynamics. ICLR Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash
2025	J	`jnl`	Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness. CoRR Tavish McDonald, Bo Lei, Stanislav Fort, Bhavya Kailkhura, Brian R. Bartoldson
2025	J	`jnl`	Modeling Code: Is Text All You Need? CoRR Daniel Nichols, Konstantinos Parasyris, Harshitha Menon, Brian R. Bartoldson, Giorgis Georgakoudis, Tal Ben-Nun, Abhinav Bhatele
2025	J	`jnl`	Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models. CoRR Siddarth Venkatraman, Vineet Jain, Sarthak Mittal, Vedant Shah, Johan S. Obando-Ceron, Yoshua Bengio, Brian R. Bartoldson, Bhavya Kailkhura, Guillaume Lajoie, Glen Berseth, Nikolay Malkin, Moksh Jain
2025	J	`jnl`	STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. CoRR Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie
2025	J	`jnl`	Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. CoRR Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein
2025	—	`conf`	Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. NAACL (Long Papers) Jacob K. Christopher, Brian R. Bartoldson, Tal Ben-Nun, Michael Cardei, Bhavya Kailkhura, Ferdinando Fioretto
2025	J	`jnl`	Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence. CoRR Sean McLeish, Ang Li, John Kirchenbauer, Dayal Singh Kalra, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Jonas Geiping, Tom Goldstein, Micah Goldblum
2025	J	`jnl`	The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. CoRR Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, John Kirchenbauer, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, Tyler Murray
2025	J	`jnl`	Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. CoRR Brian R. Bartoldson, Siddarth Venkatraman, James Diffenderfer, Moksh Jain, Tal Ben-Nun, Seanie Lee, Minsu Kim, Johan S. Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura
2024	A*	`conf`	Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. ICML Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
2024	J	`jnl`	Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. CoRR Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
2024	A*	`conf`	Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. ICML Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li
2024	J	`jnl`	Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. CoRR 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
2024	J	`jnl`	ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling. CoRR Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash
2024	A*	`conf`	NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR 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
2024	J	`jnl`	Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. CoRR Jacob K. Christopher, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto
2024	A*	`conf`	Transformers Can Do Arithmetic with the Right Embeddings. NeurIPS Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
2024	J	`jnl`	Transformers Can Do Arithmetic with the Right Embeddings. CoRR Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
2023	J	`jnl`	Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. J. Mach. Learn. Res. Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock
2023	J	`jnl`	NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR 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
2023	J	`jnl`	Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics. CoRR Brian R. Bartoldson, Yeping Hu, Amar Saini, Jose Cadena, Yucheng Fu, Jie Bao, Zhijie Xu, Brenda Ng, Phan Nguyen
2022	J	`jnl`	Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. CoRR Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock
2022	A*	`conf`	Latent Space Simulation for Carbon Capture Design Optimization. AAAI Brian R. Bartoldson, Rui Wang, Yucheng Fu, David P. Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng
2022	A*	`conf`	Models Out of Line: A Fourier Lens on Distribution Shift Robustness. NeurIPS Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer
2022	J	`jnl`	Models Out of Line: A Fourier Lens on Distribution Shift Robustness. CoRR Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer
2021	J	`jnl`	A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. CoRR James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
2021	A*	`conf`	A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. NeurIPS James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
2021	J	`jnl`	Latent Space Simulation for Carbon Capture Design Optimization. CoRR Brian R. Bartoldson, Rui Wang, Yucheng Fu, David P. Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng
2020	A*	`conf`	The Generalization-Stability Tradeoff In Neural Network Pruning. NeurIPS Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher
2019	J	`jnl`	The Generalization-Stability Tradeoff in Neural Network Pruning. CoRR Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher
2018	J	`jnl`	Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks. CoRR Brian R. Bartoldson, Adrian Barbu, Gordon Erlebacher