Learning Diverse Gaussian Graphical Models and Interpreting Edges. - Roozbeh Yousefzadeh; Diane Oyen; Nina Lanza (SDM, 2019)

Interpreting Neural Networks Using Flip Points. - Roozbeh Yousefzadeh; Dianne P. O'Leary (CoRR, 2019)

Refining the Structure of Neural Networks Using Matrix Conditioning. - Roozbeh Yousefzadeh; Dianne P. O'Leary (CoRR, 2019)

Investigating Decision Boundaries of Trained Neural Networks. - Roozbeh Yousefzadeh; Dianne P. O'Leary (CoRR, 2019)

Interpreting Machine Learning Models and Application of Homotopy Methods. - Roozbeh Yousefzadeh (2019)

Deep learning interpretation: Flip points and homotopy methods. - Roozbeh Yousefzadeh; Dianne P. O'Leary (MSML, 2020)

Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis. - Roozbeh Yousefzadeh; Dianne P. O'Leary (CoRR, 2020)

Using wavelets to analyze similarities in image datasets. - Roozbeh Yousefzadeh (CoRR, 2020)

Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets. - Roozbeh Yousefzadeh; Furong Huang (CoRR, 2020)

Deep Learning Generalization and the Convex Hull of Training Sets. - Roozbeh Yousefzadeh (CoRR, 2021)

A Sketching Method for Finding the Closest Point on a Convex Hull. - Roozbeh Yousefzadeh (CoRR, 2021)

Federated Learning without Revealing the Decision Boundaries. - Roozbeh Yousefzadeh (CoRR, 2021)

Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models. - Roozbeh Yousefzadeh; Jessica A. Mollick (CoRR, 2021)

A Homotopy Algorithm for Optimal Transport. - Roozbeh Yousefzadeh (CoRR, 2021)

Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods. - Roozbeh Yousefzadeh (MICAD, 2022)

To what extent should we trust AI models when they extrapolate? - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2022)

Decision boundaries and convex hulls in the feature space that deep learning functions learn from images. - Roozbeh Yousefzadeh (CoRR, 2022)

Deep Learning Generalization, Extrapolation, and Over-parameterization. - Roozbeh Yousefzadeh (CoRR, 2022)

Over-parameterization: A Necessary Condition for Models that Extrapolate. - Roozbeh Yousefzadeh (CoRR, 2022)

Should Machine Learning Models Report to Us When They Are Clueless? - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2022)

Large Language Models' Understanding of Math: Source Criticism and Extrapolation. - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2023)

An Ambiguity Measure for Recognizing the Unknowns in Deep Learning. - Roozbeh Yousefzadeh (CoRR, 2023)

Extrapolation and AI transparency: Why machine learning models should reveal when they make decisions beyond their training. - Xuenan Cao; Roozbeh Yousefzadeh (Big Data Soc., 2023)

A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems. - Roozbeh Yousefzadeh; Xuenan Cao (Trans. Mach. Learn. Res., 2025)

APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning. - Azim Ospanov; Farzan Farnia; Roozbeh Yousefzadeh (CoRR, 2025)

Mathesis: Towards Formal Theorem Proving from Natural Languages. - Yu Xuejun; Jianyuan Zhong; Zijin Feng; Pengyi Zhai; Roozbeh Yousefzadeh; Wei Chong Ng; Haoxiong Liu; Ziyi Shou; Jing Xiong; Yudong Zhou; Claudia Ong; Austen Jeremy Sugiarto; et al. (CoRR, 2025)

Advocate for Complete Benchmarks for Formal Reasoning with Formal/Informal Statements and Formal/Informal Proofs. - Roozbeh Yousefzadeh; Xuenan Cao (CoRR, 2025)