Ardavan Saeedi

ORCID

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 19

Top Venues

Massachusetts Institute of Technology, Cambridge, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, USA

PHD

Publications

Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh, Sanjit S. Batra, Jeffrey Hertzberg, Jaime Murillo, Eran Halperin
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering. CoRR (2023)
Anna Wong, Shu Ge, Nassim Oufattole, Adam Dejl, Megan Su, Ardavan Saeedi, Li-Wei H. Lehman
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series. CoRR (2023)
Ardavan Saeedi, Yuria Utsumi, Li Sun, Kayhan Batmanghelich, Li-Wei H. Lehman
Knowledge Distillation via Constrained Variational Inference. AAAI (2022)
Adam Dejl, Harsh Deep, Jonathan Fei, Ardavan Saeedi, Li-Wei H. Lehman
Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes. CoRR (2022)
Ardavan Saeedi, Payman Yadollahpour, Sumedha Singla, Brian Pollack, William M. Wells III, Frank C. Sciurba, Kayhan Batmanghelich
Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach. MLHC (2021)
Igor Lovchinsky, Alon Daks, Israel Malkin, Pouya Samangouei, Ardavan Saeedi, Yang Liu, Swami Sankaranarayanan, Tomer Gafner, Ben Sternlieb, Patrick Maher, Nathan Silberman
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth. ICLR (2020)
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion. CoRR (2019)
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman
Learning From Noisy Labels by Regularized Estimation of Annotator Confusion. CVPR (2019)
Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations. ECCV (10) (2018)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS (2018)
Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman
Variational Particle Approximations. J. Mach. Learn. Res. 18 (2017)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR (2017)
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman
Deep Successor Reinforcement Learning. CoRR (2016)
Kayhan N. Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Samuel Gershman
Nonparametric Spherical Topic Modeling with Word Embeddings. CoRR (2016)
Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. CoRR (2016)
Kayhan N. Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Samuel Gershman
Nonparametric Spherical Topic Modeling with Word Embeddings. ACL (2) (2016)
Nematollah Kayhan Batmanghelich, Ardavan Saeedi, Raúl San José Estépar, Michael H. Cho, William M. Wells III
Inferring Disease Status by Non-parametric Probabilistic Embedding. MCV/BAMBI@MICCAI (2016)
Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML (2016)
Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. NIPS (2016)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML (2015)
Nematollah Kayhan Batmanghelich, Ardavan Saeedi, Michael H. Cho, Raúl San José Estépar, Polina Golland
Generative Method to Discover Genetically Driven Image Biomarkers. IPMI (2015)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR (2015)
Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR (2015)
Tejas D. Kulkarni, Ardavan Saeedi, Samuel Gershman
Variational Particle Approximations. CoRR (2014)
Ardavan Saeedi, Alexandre Bouchard-Côté
Priors over Recurrent Continuous Time Processes. NIPS (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ardavan Saeedi