38 citations · 109 across the 14 of their papers we have counts for
16 papers
Bayes Rule for Conditioning Probabilistic Programs
Feras A. Saad
Probabilistic programs provide a rigorous programming-language foundation for expressing rich probabilistic models and offer language-level support for solving challenging statisti…
Probabilistic Seasonality
Feras A. Saad, Todd B. Walker
Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery me…
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
Mathieu Huot, Matin Ghavami, Alexander K. Lew +6
This article presents GenSQL, a probabilistic programming system for querying probabilistic generative models of database tables. By augmenting SQL with only a few key primitives f…
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman +2
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic space of…
Estimators of Entropy and Information via Inference in Probabilistic Models
Feras A. Saad, Marco Cusumano-Towner, Vikash K. Mansinghka
Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimension…
Hierarchical Infinite Relational Model
Feras A. Saad, Vikash K. Mansinghka
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of re…
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inf…
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions
Feras A. Saad, Cameron E. Freer, Martin C. Rinard +1
This paper introduces a new algorithm for the fundamental problem of generating a random integer from a discrete probability distribution using a source of independent and unbiased…
Optimal Approximate Sampling from Discrete Probability Distributions
Feras A. Saad, Cameron E. Freer, Martin C. Rinard +1
This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and…
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle +2
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-s…
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman +1
The objective of goodness-of-fit testing is to assess whether a dataset of observations is likely to have been drawn from a candidate probability distribution. This paper presents…
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
Feras A. Saad, Vikash K. Mansinghka
This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data. The method is appropriate for…
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Feras Saad, Leonardo Casarsa, Vikash Mansinghka
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative resu…
Time Series Structure Discovery via Probabilistic Program Synthesis
Ulrich Schaechtle, Feras Saad, Alexey Radul +1
There is a widespread need for techniques that can discover structure from time series data. Recently introduced techniques such as Automatic Bayesian Covariance Discovery (ABCD) p…
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Feras Saad, Vikash Mansinghka
Datasets with hundreds of variables and many missing values are commonplace. In this setting, it is both statistically and computationally challenging to detect true predictive rel…
Probabilistic Data Analysis with Probabilistic Programming
Feras Saad, Vikash Mansinghka
Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative populat…