Diffenderfer, James

17 publications

NeurIPS 2025 BOOM: Benchmarking Out-of-Distribution Molecular Property Predictions of Machine Learning Models Evan R Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Busra Sahin, Obadiah Hersh Smolenski, Everett Grethel, Tim Hsu, Anna Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen

MLJ 2025 Deep Learning of PDE Correction and Mesh Adaption Without Automatic Differentiation Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou

NeurIPS 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, Johan Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura

ICCV 2025 TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination via Latent Truthful-Guided Pre-Intervention Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

TMLR 2025 When SNN Meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter Anthony Beerel

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

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

ICMLW 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, Dawn Song, Zhangyang Wang, Bo Li

ICLRW 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, Dawn Song, Zhangyang Wang, Bo Li

ICLR 2024 DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu

NeurIPS 2024 GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu

NeurIPS 2023 Neural Image Compression: Generalization, Robustness, and Spectral Biases Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

ICMLW 2023 Neural Image Compression: Generalization, Robustness, and Spectral Biases Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

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

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

ICLR 2021 Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning a Randomly Weighted Network James Diffenderfer, Bhavya Kailkhura