Priors over Recurrent Continuous Time Processes. - Ardavan Saeedi; Alexandre Bouchard-Côté (NIPS, 2011)

Variational Particle Approximations. - Tejas D. Kulkarni; Ardavan Saeedi; Samuel Gershman (CoRR, 2014)

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. - Jonathan H. Huggins; Karthik Narasimhan; Ardavan Saeedi; Vikash Mansinghka 001 (ICML, 2015)

Generative Method to Discover Genetically Driven Image Biomarkers. - Nematollah Kayhan Batmanghelich; Ardavan Saeedi; Michael H. Cho; Raúl San José Estépar; Polina Golland (IPMI, 2015)

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. - Jonathan H. Huggins; Ardavan Saeedi; Matthew J. Johnson 002 (CoRR, 2015)

Nonparametric Spherical Topic Modeling with Word Embeddings. - Kayhan N. Batmanghelich; Ardavan Saeedi; Karthik Narasimhan; Samuel Gershman (ACL (2), 2016)

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. - Ardavan Saeedi; Matthew D. Hoffman; Matthew J. Johnson 002; Ryan P. Adams (ICML, 2016)

Inferring Disease Status by Non-parametric Probabilistic Embedding. - Nematollah Kayhan Batmanghelich; Ardavan Saeedi; Raúl San José Estépar; Michael H. Cho; William M. Wells III (MCV/BAMBI@MICCAI, 2016)

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. - Tejas D. Kulkarni; Karthik Narasimhan; Ardavan Saeedi; Josh Tenenbaum 001 (NIPS, 2016)

Deep Successor Reinforcement Learning. - Tejas D. Kulkarni; Ardavan Saeedi; Simanta Gautam; Samuel J. Gershman (CoRR, 2016)

Variational Particle Approximations. - Ardavan Saeedi; Tejas D. Kulkarni; Vikash K. Mansinghka; Samuel J. Gershman (J. Mach. Learn. Res., 2017)

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. - Ardavan Saeedi; Matthew D. Hoffman; Stephen J. DiVerdi; Asma Ghandeharioun; Matthew J. Johnson 002; Ryan P. Adams (AISTATS, 2018)

ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations. - Pouya Samangouei; Ardavan Saeedi; Liam Nakagawa; Nathan Silberman (ECCV (10), 2018)

Latent variable models for understanding user behavior in software applications. - Ardavan Saeedi (2018)

Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. - Ryutaro Tanno; Ardavan Saeedi; Swami Sankaranarayanan; Daniel C. Alexander; Nathan Silberman (CVPR, 2019)

Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. - Igor Lovchinsky; Alon Daks; Israel Malkin; Pouya Samangouei; Ardavan Saeedi; Yang Liu; Swami Sankaranarayanan; Tomer Gafner; Ben Sternlieb; Patrick Maher; Nathan Silberman (ICLR, 2020)

Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach. - Ardavan Saeedi; Payman Yadollahpour; Sumedha Singla; Brian Pollack; William M. Wells III; Frank C. Sciurba; Kayhan Batmanghelich (MLHC, 2021)

Knowledge Distillation via Constrained Variational Inference. - Ardavan Saeedi; Yuria Utsumi; Li Sun 010; Kayhan Batmanghelich; Li-Wei H. Lehman (AAAI, 2022)

Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes. - Adam Dejl; Harsh Deep; Jonathan Fei; Ardavan Saeedi; Li-Wei H. Lehman (CoRR, 2022)

A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series. - Anna Wong; Shu Ge; Nassim Oufattole; Adam Dejl; Megan Su; Ardavan Saeedi; Li-Wei H. Lehman (CoRR, 2023)

XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering. - Joel Stremmel; Ardavan Saeedi; Hamid Hassanzadeh; Sanjit S. Batra; Jeffrey Hertzberg; Jaime Murillo; Eran Halperin (CoRR, 2023)

Give me Some Hard Questions: Synthetic Data Generation for Clinical QA. - Fan Bai 006; Keith Harrigian; Joel Stremmel; Hamid Hassanzadeh; Ardavan Saeedi; Mark Dredze (CoRR, 2024)

Label-Guided In-Context Learning for Named Entity Recognition. - Fan Bai 006; Hamid Hassanzadeh; Ardavan Saeedi; Mark Dredze (CoRR, 2025)