Vikash Mansinghka

Publication Activity (10 Years)

Years Active: 2014-2021
Publications (10 Years): 15

Top Topics

Causal Inference

Multivariate Time Series

Gaussian Process

Top Venues

J. Mach. Learn. Res.

Publications

Nicholas Roy, Ingmar Posner, Tim D. Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Daniel E. Koditschek, Tomás Lozano-Pérez, Vikash Mansinghka, Christopher J. Pal, Blake A. Richards, Dorsa Sadigh, Stefan Schaal, Gaurav S. Sukhatme, Denis Thérien, Marc Toussaint, Michiel van de Panne
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR (2021)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS (2021)
Sam Witty, David D. Jensen, Vikash Mansinghka
A Simulation-Based Test of Identifiability for Bayesian Causal Inference. CoRR (2021)
Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka
Online Bayesian Goal Inference for Boundedly Rational Planning Agents. NeurIPS (2020)
Alexander K. Lew, Michael Henry Tessler, Vikash Mansinghka, Josh Tenenbaum
Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. CogSci (2020)
Feras Saad, Cameron E. Freer, Martin Rinard, Vikash Mansinghka
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. AISTATS (2020)
Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
Causal Inference using Gaussian Processes with Structured Latent Confounders. ICML (2020)
Span Spanbauer, Cameron E. Freer, Vikash Mansinghka
Deep Involutive Generative Models for Neural MCMC. CoRR (2020)
Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
Causal Inference using Gaussian Processes with Structured Latent Confounders. CoRR (2020)
Javier Felip, Nilesh A. Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka
Real-time Approximate Bayesian Computation for Scene Understanding. CoRR (2019)
Shivam Handa, Vikash Mansinghka, Martin Rinard
Compositional Inference Metaprogramming with Convergence Guarantees. CoRR (2019)
Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka
Bayesian causal inference via probabilistic program synthesis. CoRR (2019)
Feras Saad, Vikash Mansinghka
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. AISTATS (2018)
Feras Saad, Vikash Mansinghka
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. AISTATS (2017)
Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. J. Mach. Learn. Res. 17 (2016)
Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
A New Approach to Probabilistic Programming Inference. CoRR (2015)
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS (2015)
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR (2015)
Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
A New Approach to Probabilistic Programming Inference. AISTATS (2014)