Google Scholar
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Sequential Monte Carlo Learning for Time Series Structure Discovery
F. Saad, B. Patton, M. Hoffman, R. Saurous, V. Mansinghka
ICML, Proc. 40th International Conference on Machine Learning, 2023
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Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
F. Saad
PhD Thesis, Massachusetts Institute of Technology, 2022
MIT George M. Sprowls PhD Thesis Award
Estimators of Entropy and Information via Inference in Probabilistic Models
F. Saad, M. Cusumano-Towner, V. Mansinghka
AISTATS, Proc. 25th International Conference on Artificial Intelligence and Statistics, 2022
paper
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| arXiv
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Bayesian AutoML for Databases via the InferenceQL Probabilistic Programming System
U. Schaechtle, C. Freer, Z. Shelby, F. Saad, V. Mansinghka
AutoML, 1st International Conference on Automated Machine Learning (Late-Breaking Workshop), 2022
paper
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SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
F. Saad, M. Rinard, Mansinghka
PLDI, Proc. 42nd International Conference on Programming Design and Implementation, 2021
paper
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Hierarchical Infinite Relational Model
F. Saad, V. Mansinghka
UAI, Proc. 37th Conference on Uncertainty in Artificial Intelligence, 2021
Oral Presentation
paper
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| arXiv
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The Fast Loaded Dice Roller: A Near Optimal Exact Sampler for Discrete Probability Distributions
F. Saad, C. Freer, M. Rinard, V. Mansinghka
AISTATS, Proc. 24th International Conference on Artificial Intelligence and Statistics, 2020
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Optimal Approximate Sampling from Discrete Probability Distributions
F. Saad, C. Freer, M. Rinard, V. Mansinghka
POPL, Proc. ACM Program. Lang. 4(POPL), 2020
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| arXiv
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A Family of Exact Goodness-of-Fit Tests for High-dimensional Discrete Distributions
F. Saad, C. Freer, N. Ackerman, V. Mansinghka
AISTATS, Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2019
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| arXiv
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Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
F. Saad, M. Cusumano-Towner, U. Schaechtle, M. Rinard, V. Mansinghka
POPL, Proc. ACM Program. Lang. 3, 2020
paper
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| arXiv
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Gen: A General Purpose Probabilistic Programming System with Programmable Inference
M. Cusumano-Towner, F. Saad, A. Lew, Mansinghka
PLDI, Proc. 40th International Conference on Programming Design and Implementation, 2019
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Elements of a Stochastic 3D Prediction Engine in Larval Zebrafish Prey Capture
A. Bolton, M. Haesemeyer, J. Jordi, U. Schaechtle, F. Saad, V. Mansinghka, J. Tenenbaum, F. Engert
eLife 8:e51975, 2019
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Temporally-Reweighted Chinese Restaurant Process Mixtures
for Clustering, Imputing, and Forecasting Multivariate Time Series
F. Saad, V. Mansinghka
AISTATS, Proc. 21st International Conference on Artificial Intelligence and Statistics, 2018
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Goodness-of-Fit Tests for High-dimensional Discrete Distributions
with Application to Convergence Diagnostics in Approximate Bayesian Inference
F. Saad, C. Freer, N. Ackerman, V. Mansinghka
AABI, 1st Symposium on Advances in Approximate Bayesian Inference, 2018
paper
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Detecting Dependencies in Sparse, Multivariate Databases
Using Probabilistic Programming and Non-parametric Bayes
F. Saad, V. Mansinghka
AISTATS, Proc. 20th International Conference on Artificial Intelligence and Statistics, 2017
paper
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| arXiv
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Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
F. Saad, L. Casarsa, V. Mansinghka
arXiv, Technical Report arXiv:1704.01087, 2017
PROBPROG, 1st International Conference for Probabilistic Programming, 2018
arXiv
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Time Series Structure Discovery via Probabilistic Program Synthesis
U. Schaechtle*, F. Saad*, A. Radul, V. Mansinghka
arXiv, Technical Report arXiv:1611.07051, 2017
PROBPROG, 1st International Conference for Probabilistic Programming, 2018
arXiv
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A Probabilistic Programming Approach to Probabilistic Data Analysis
F. Saad, V. Mansinghka
NIPS, Proc. 30th Conference on Neural Information Processing Systems, 2016
paper
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Probabilistic Data Analysis with Probabilistic Programming
F. Saad, V. Mansinghka
arXiv, Technical Report arXiv:1608.05347, 2016
Extended version of NIPS 2016.
arXiv
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Probabilistic Data Analysis with Probabilistic Programming
F. Saad
MEng Thesis, Massachusetts Institute of Technology, 2016
MIT Charles & Jennifer Johnson MEng Thesis Award, 1st Place