Building a Better Bijection Between Classes of Compositions. - James Daniel Diffenderfer (Integers, 2014)

Construction of a full row-rank matrix system for multiple scanning directions in discrete tomography. - Xiezhang Li; James Diffenderfer; Jiehua Zhu (J. Comput. Appl. Math., 2017)

Error Analysis of ZFP Compression for Floating-Point Data. - James Diffenderfer; Alyson Fox; Jeffrey A. F. Hittinger; Geoffrey Sanders; Peter G. Lindstrom (SIAM J. Sci. Comput., 2019)

Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods. - Alyson Fox; James Diffenderfer; Jeffrey Hittinger; Geoffrey Sanders; Peter Lindstrom 001 (SIAM J. Sci. Comput., 2020)

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

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)

HPAC: evaluating approximate computing techniques on HPC OpenMP applications. - Konstantinos Parasyris; Giorgis Georgakoudis; Harshitha Menon; James Diffenderfer; Ignacio Laguna; Daniel Osei-Kuffuor; Markus Schordan (SC, 2021)

QDOT: Quantized Dot Product Kernel for Approximate High-Performance Computing. - James Diffenderfer; Daniel Osei-Kuffuor; Harshitha Menon (CoRR, 2021)

Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study. - Kshitij Bhardwaj; James Diffenderfer; Bhavya Kailkhura; Maya B. Gokhale (DATE, 2022)

Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. - Kshitij Bhardwaj; James Diffenderfer; Bhavya Kailkhura; Maya B. Gokhale (ISPASS, 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)

Approximate Computing Through the Lens of Uncertainty Quantification. - Konstantinos Parasyris; James Diffenderfer; Harshitha Menon; Ignacio Laguna; Jackson Vanover; Ryan Vogt; Daniel Osei-Kuffuor (SC, 2022)

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. - Ioannis C. Tsaknakis; Bhavya Kailkhura; Sijia Liu 001; Donald Loveland; James Diffenderfer; Anna Maria Hiszpanski; Mingyi Hong 001 (CoRR, 2022)

"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (CoRR, 2022)

Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed. - Hao Cheng 015; Pu Zhao 001; Yize Li; Xue Lin 001; James Diffenderfer; Ryan A. Goldhahn; Bhavya Kailkhura (CoRR, 2022)

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products. - James Diffenderfer; Daniel Osei-Kuffuor; Harshitha Menon (SIAM J. Sci. Comput., 2022)

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

When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks. - Gourav Datta; Zeyu Liu 003; James Diffenderfer; Bhavya Kailkhura; Peter A. Beerel (CoRR, 2023)

Approximate High-Performance Computing: A Fast and Energy-Efficient Computing Paradigm in the Post-Moore Era. - Harshitha Menon; James Diffenderfer; Giorgis Georgakoudis; Ignacio Laguna; Michael O. Lam; Daniel Osei-Kuffuor; Konstantinos Parasyris; Jackson Vanover (IT Prof., 2023)

SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. - Jinghan Jia; Yihua Zhang; Yimeng Zhang; Jiancheng Liu; Bharat Runwal; James Diffenderfer; Bhavya Kailkhura; Sijia Liu 001 (EMNLP, 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 005; Bhavya Kailkhura; Sijia Liu 001 (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)

ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models. - Jinhao Duan; Shiqi Wang 002; James Diffenderfer; Lichao Sun 001; Tianlong Chen 001; Bhavya Kailkhura; Kaidi Xu (NAACL-HLT, 2024)

GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations. - Jinhao Duan; Renming Zhang; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Elias Stengel-Eskin; Mohit Bansal; Tianlong Chen 001; Kaidi Xu (NeurIPS, 2024)

GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations. - Jinhao Duan; Renming Zhang; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Elias Stengel-Eskin; Mohit Bansal; Tianlong Chen 001; Kaidi Xu (CoRR, 2024)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (CoRR, 2024)

ZFP: A compressed array representation for numerical computations. - Peter Lindstrom 001; Jeffrey Hittinger; James Diffenderfer; Alyson Fox; Daniel Osei-Kuffuor; Jeffrey W. Banks (Int. J. High Perform. Comput. Appl., 2025)

When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency. - Gourav Datta; Zeyu Liu 003; James Diffenderfer; Bhavya Kailkhura; Peter Anthony Beerel (Trans. Mach. Learn. Res., 2025)

TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention. - Jinhao Duan; Fei Kong; Hao Cheng 015; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Xiaofeng Zhu 001; Xiaoshuang Shi; Kaidi Xu (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)

Deep learning of PDE correction and mesh adaption without automatic differentiation. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (Mach. Learn., 2025)

LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks. - Soumyadeep Pal; Changsheng Wang; James Diffenderfer; Bhavya Kailkhura; Sijia Liu 001 (CoRR, 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 Demirci; Obadiah Smolenski; Tim Hsu; Anna M. Hiszpanski; Kenneth Chiu; Bhavya Kailkhura; Brian Van Essen (CoRR, 2025)

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making. - Jinhao Duan; James Diffenderfer; Sandeep Madireddy; Tianlong Chen 001; Bhavya Kailkhura; Kaidi Xu (CoRR, 2025)

"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2025)