Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks. - Brian R. Bartoldson; Adrian Barbu; Gordon Erlebacher (CoRR, 2018)

The Generalization-Stability Tradeoff In Neural Network Pruning. - Brian R. Bartoldson; Ari S. Morcos; Adrian Barbu; Gordon Erlebacher (NeurIPS, 2020)

A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. - James Diffenderfer; Brian R. Bartoldson; Shreya Chaganti; Jize Zhang; Bhavya Kailkhura (NeurIPS, 2021)

Latent Space Simulation for Carbon Capture Design Optimization. - Brian R. Bartoldson; Rui Wang; Yucheng Fu; David P. Widemann; Sam Nguyen; Jie Bao; Zhijie Xu; Brenda Ng (AAAI, 2022)

Models Out of Line: A Fourier Lens on Distribution Shift Robustness. - Sara Fridovich-Keil; Brian R. Bartoldson; James Diffenderfer; Bhavya Kailkhura; Timo Bremer (NeurIPS, 2022)

Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics. - Brian R. Bartoldson; Yeping Hu; Amar Saini; Jose Cadena; Yucheng Fu; Jie Bao; Zhijie Xu; Brenda Ng; Phan Nguyen (CoRR, 2023)

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. - Brian R. Bartoldson; Bhavya Kailkhura; Davis W. Blalock (J. Mach. Learn. Res., 2023)

NEFTune: Noisy Embeddings Improve Instruction Finetuning. - 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; et al. (ICLR, 2024)

Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. - Brian R. Bartoldson; James Diffenderfer; Konstantinos Parasyris; Bhavya Kailkhura (ICML, 2024)

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. - 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; et al. (ICML, 2024)

Transformers Can Do Arithmetic with the Right Embeddings. - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (NeurIPS, 2024)

ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling. - Haizhong Zheng; Elisa Tsai; Yifu Lu; Jiachen Sun; Brian R. Bartoldson; Bhavya Kailkhura; Atul Prakash 001 (CoRR, 2024)

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. - Jacob K. Christopher; Brian R. Bartoldson; Bhavya Kailkhura; Ferdinando Fioretto (CoRR, 2024)

ELFS: Label-Free Coreset Selection with Proxy Training Dynamics. - Haizhong Zheng; Elisa Tsai; Yifu Lu; Jiachen Sun; Brian R. Bartoldson; Bhavya Kailkhura; Atul Prakash 001 (ICLR, 2025)

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. - Jacob K. Christopher; Brian R. Bartoldson; Tal Ben-Nun; Michael Cardei; Bhavya Kailkhura; Ferdinando Fioretto (NAACL (Long Papers), 2025)

Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness. - Zeyu Wang 008; Cihang Xie; Brian R. Bartoldson; Bhavya Kailkhura (CoRR, 2025)

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. - Jonas Geiping; Sean McLeish; Neel Jain; John Kirchenbauer; Siddharth Singh; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Tom Goldstein (CoRR, 2025)

Constrained Language Generation with Discrete Diffusion Models. - Michael Cardei; Jacob K. Christopher; Thomas Hartvigsen; Brian R. Bartoldson; Bhavya Kailkhura; Ferdinando Fioretto (CoRR, 2025)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. - Brian R. Bartoldson; Siddarth Venkatraman; James Diffenderfer; Moksh Jain; Tal Ben-Nun; Seanie Lee; Minsu Kim 004; Johan S. Obando-Ceron; Yoshua Bengio; Bhavya Kailkhura (CoRR, 2025)

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. - Zijun Wang; Haoqin Tu; Yuhan Wang; Juncheng Wu; Jieru Mei; Brian R. Bartoldson; Bhavya Kailkhura; Cihang Xie (CoRR, 2025)

AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security. - Zikui Cai; Shayan Shabihi; Bang An 001; Zora Che; Brian R. Bartoldson; Bhavya Kailkhura; Tom Goldstein; Furong Huang (CoRR, 2025)

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts. - Haizhong Zheng; Yang Zhou; Brian R. Bartoldson; Bhavya Kailkhura; Fan Lai 001; Jiawei Zhao; Beidi Chen (CoRR, 2025)

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. - 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; et al. (CoRR, 2025)

Modeling Code: Is Text All You Need? - Daniel Nichols; Konstantinos Parasyris; Harshitha Menon; Brian R. Bartoldson; Giorgis Georgakoudis; Tal Ben-Nun; Abhinav Bhatele (CoRR, 2025)