Gen: a general-purpose probabilistic programming system with programmable inference. - Marco F. Cusumano-Towner; Feras A. Saad; Alexander K. Lew; Vikash K. Mansinghka (PLDI, 2019)

Bayesian causal inference via probabilistic program synthesis. - Sam Witty; Alexander K. Lew; David D. Jensen; Vikash Mansinghka 001 (CoRR, 2019)

Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. - Alexander K. Lew; Michael Henry Tessler; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 2020)

Trace types and denotational semantics for sound programmable inference in probabilistic languages. - Alexander K. Lew; Marco F. Cusumano-Towner; Benjamin Sherman; Michael Carbin; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2020)

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. - Alexander K. Lew; Monica Agrawal; David A. Sontag; Vikash Mansinghka 001 (AISTATS, 2021)

Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages. - Alexander K. Lew; Mathieu Huot; Vikash K. Mansinghka (CoRR, 2021)

Recursive Monte Carlo and variational inference with auxiliary variables. - Alexander K. Lew; Marco F. Cusumano-Towner; Vikash K. Mansinghka (UAI, 2022)

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. - Alexander K. Lew; George Matheos; Tan Zhi-Xuan; Matin Ghavamizadeh; Nishad Gothoskar; Stuart Russell 001; Vikash K. Mansinghka (AISTATS, 2023)

ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. - Mathieu Huot; Alexander K. Lew; Vikash K. Mansinghka; Sam Staton (LICS, 2023)

Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs. - Alexander K. Lew; Tan Zhi-Xuan; Gabriel Grand; Vikash K. Mansinghka (CoRR, 2023)

Differentiating Metropolis-Hastings to Optimize Intractable Densities. - Gaurav Arya; Ruben Seyer; Frank Schäfer; Alexander K. Lew; Mathieu Huot; Vikash K. Mansinghka; Chris Rackauckas; Kartik Chandra; Moritz Schauer (CoRR, 2023)

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. - Lionel Wong; Gabriel Grand; Alexander K. Lew; Noah D. Goodman; Vikash K. Mansinghka; Jacob Andreas; Joshua B. Tenenbaum (CoRR, 2023)

Probabilistic Programming with Stochastic Probabilities. - Alexander K. Lew; Matin Ghavamizadeh; Martin C. Rinard; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2023)

ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. - Alexander K. Lew; Mathieu Huot; Sam Staton; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2023)

Probabilistic Programming with Programmable Variational Inference. - McCoy R. Becker; Alexander K. Lew; Xiaoyan Wang; Matin Ghavami; Mathieu Huot; Martin C. Rinard; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2024)

GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. - Mathieu Huot; Matin Ghavami; Alexander K. Lew; Ulrich Schaechtle; Cameron E. Freer; Zane Shelby; Martin C. Rinard; Feras A. Saad; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2024)

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 004; Yahya Emara; Marjorie Freedman; Jason Eisner; Ryan Cotterell; Vikash Mansinghka 001; et al. (ICLR, 2025)

Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs. - Maddy Bowers; Alexander K. Lew; Joshua B. Tenenbaum; Armando Solar-Lezama; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2025)

Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling. - Benjamin Lipkin; Benjamin LeBrun; Jacob Hoover Vigly; João Loula; David R. MacIver; Li Du; Jason Eisner; Ryan Cotterell; Vikash Mansinghka 001; Timothy J. O'Donnell; Alexander K. Lew; Tim Vieira (CoRR, 2025)

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

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models. - Lionel Wong; Katherine M. Collins; Lance Ying; Cedegao E. Zhang; Adrian Weller; Tobias Gerstenberg; Timothy O'Donnell; Alexander K. Lew; Jacob Andreas; Joshua B. Tenenbaum; Tyler Brooke-Wilson (CoRR, 2025)