Mansinghka, Vikash

19 publications

ICLRW 2025 Self-Steering Language Models Gabriel Grand, Joshua B. Tenenbaum, Vikash Mansinghka, Alexander K. Lew, Jacob Andreas

ICLR 2025 Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell

ICMLW 2024 Learning Generative Population Models from Multiple Clinical Datasets via Probabilistic Programming João Loula, Katherine M. Collins, Ulrich Schaechtle, Joshua B. Tenenbaum, Adrian Weller, Feras Saad, Timothy J. O'Donnell, Vikash Mansinghka

ICMLW 2023 Differentiating Metropolis-Hastings to Optimize Intractable Densities Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash Mansinghka, Jonathan Ragan-Kelley, Christopher Vincent Rackauckas, Moritz Schauer

ICMLW 2023 Inferring the Goals of Communicating Agents from Actions and Instructions Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka, Joshua B. Tenenbaum

ICML 2023 Sequential Monte Carlo Learning for Time Series Structure Discovery Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka

ICMLW 2023 Sequential Monte Carlo Steering of Large Language Models Using Probabilistic Programs Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash Mansinghka

AISTATS 2022 Estimators of Entropy and Information via Inference in Probabilistic Models Feras Saad, Marco Cusumano-Towner, Vikash Mansinghka

AISTATS 2021 PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming Alexander Lew, Monica Agrawal, David Sontag, Vikash Mansinghka

ICML 2020 Causal Inference Using Gaussian Processes with Structured Latent Confounders Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

AISTATS 2020 The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions Feras Saad, Cameron Freer, Martin Rinard, Vikash Mansinghka

AISTATS 2018 Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series Feras Saad, Vikash Mansinghka

AISTATS 2017 Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-Parametric Bayes Feras Saad, Vikash Mansinghka

JMLR 2016 CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum

ICML 2015 JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

AISTATS 2015 Particle Gibbs with Ancestor Sampling for Probabilistic Programs Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood

CVPR 2015 Picture: A Probabilistic Programming Language for Scene Perception Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka

AISTATS 2014 A New Approach to Probabilistic Programming Inference Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka

AISTATS 2009 Exact and Approximate Sampling by Systematic Stochastic Search Vikash Mansinghka, Daniel Roy, Eric Jonas, Joshua Tenenbaum