A Brief Note on the Convergence of Langevin Monte Carlo in Chi-Square Divergence. - Murat A. Erdogdu; Rasa Hosseinzadeh (CoRR, 2020)

On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness. - Murat A. Erdogdu; Rasa Hosseinzadeh (COLT, 2021)

Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. - Murat A. Erdogdu; Rasa Hosseinzadeh; Shunshi Zhang 001 (AISTATS, 2022)

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers. - Sajad Norouzi; Rasa Hosseinzadeh; Felipe Pérez; Maksims Volkovs (CoRR, 2022)

DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation. - Sajad Norouzi; Rasa Hosseinzadeh; Felipe Pérez; Maksims Volkovs (ACL (Findings), 2023)

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. - George Stein; Jesse C. Cresswell; Rasa Hosseinzadeh; Yi Sui 001; Brendan Leigh Ross; Valentin Villecroze; Zhaoyan Liu; Anthony L. Caterini; J. Eric T. Taylor; Gabriel Loaiza-Ganem (NeurIPS, 2023)

A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. - Hamidreza Kamkari; Brendan Leigh Ross; Rasa Hosseinzadeh; Jesse C. Cresswell; Gabriel Loaiza-Ganem (NeurIPS, 2024)

Retrieval & Fine-Tuning for In-Context Tabular Models. - Valentin Thomas; Junwei Ma; Rasa Hosseinzadeh; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (NeurIPS, 2024)

Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation. - Wei Cui; Rasa Hosseinzadeh; Junwei Ma; Tongzi Wu; Yi Sui; Keyvan Golestan (CoRR, 2024)

TabDPT: Scaling Tabular Foundation Models. - Junwei Ma; Valentin Thomas; Rasa Hosseinzadeh; Hamidreza Kamkari; Alex Labach; Jesse C. Cresswell; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (CoRR, 2024)

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples. - Noël Vouitsis; Rasa Hosseinzadeh; Brendan Leigh Ross; Valentin Villecroze; Satya Krishna Gorti; Jesse C. Cresswell; Gabriel Loaiza-Ganem (CoRR, 2024)

Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. - Gabriel Loaiza-Ganem; Brendan Leigh Ross; Rasa Hosseinzadeh; Anthony L. Caterini; Jesse C. Cresswell (Trans. Mach. Learn. Res., 2024)

A Geometric Framework for Understanding Memorization in Generative Models. - Brendan Leigh Ross; Hamidreza Kamkari; Tongzi Wu; Rasa Hosseinzadeh; Zhaoyan Liu; George Stein; Jesse C. Cresswell; Gabriel Loaiza-Ganem (ICLR, 2025)

MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation. - Satya Krishna Gorti; Ilan Gofman; Zhaoyan Liu; Jiapeng Wu; Noël Vouitsis; Guangwei Yu; Jesse C. Cresswell; Rasa Hosseinzadeh (NAACL (Long Papers), 2025)

On Convolutions, Intrinsic Dimension, and Diffusion Models. - Kin Kwan Leung; Rasa Hosseinzadeh; Gabriel Loaiza-Ganem (Trans. Mach. Learn. Res., 2025)

Textual Bayes: Quantifying Uncertainty in LLM-Based Systems. - Brendan Leigh Ross; Noël Vouitsis; Atiyeh Ashari Ghomi; Rasa Hosseinzadeh; Ji Xin; Zhaoyan Liu; Yi Sui 001; Shiyi Hou; Kin Kwan Leung; Gabriel Loaiza-Ganem; Jesse C. Cresswell (CoRR, 2025)