| Gen: a general-purpose probabilistic programming system with programmable inference MF Cusumano-Towner, FA Saad, AK Lew, VK Mansinghka Proceedings of the 40th ACM SIGPLAN Conference on Programming Language …, 2019 | 201 | 2019 |
| Few-shot Bayesian imitation learning with logical program policies T Silver, KR Allen, AK Lew, LP Kaelbling, J Tenenbaum AAAI, 10251-10258, 2020 | 41* | 2020 |
| From word models to world models: Translating from natural language to the probabilistic language of thought L Wong, G Grand, AK Lew, ND Goodman, VK Mansinghka, J Andreas, ... arXiv preprint arXiv:2306.12672, 2023 | 39 | 2023 |
| Trace types and denotational semantics for sound programmable inference in probabilistic languages AK Lew, MF Cusumano-Towner, B Sherman, M Carbin, VK Mansinghka Proceedings of the ACM on Programming Languages 4 (POPL), 1-32, 2020 | 39 | 2020 |
| PClean: Bayesian data cleaning at scale with domain-specific probabilistic programming AK Lew, M Agrawal, D Sontag, VK Mansinghka International Conference on Artificial Intelligence and Statistics, 1927-1935, 2021 | 22 | 2021 |
| Automating involutive MCMC using probabilistic and differentiable programming M Cusumano-Towner, AK Lew, VK Mansinghka arXiv preprint arXiv:2007.09871, 2020 | 19 | 2020 |
| ADEV: Sound automatic differentiation of expected values of probabilistic programs AK Lew*, M Huot*, S Staton, VK Mansinghka Proceedings of the ACM on Programming Languages 7 (POPL), 121-153, 2023 | 16 | 2023 |
| SMCP3: Sequential Monte Carlo with probabilistic program proposals AK Lew*, G Matheos*, T Zhi-Xuan, M Ghavamizadeh, N Gothoskar, ... International Conference on Artificial Intelligence and Statistics, 7061-7088, 2023 | 10 | 2023 |
| Recursive Monte Carlo and variational inference with auxiliary variables AK Lew, M Cusumano-Towner, VK Mansinghka The 38th Conference on Uncertainty in Artificial Intelligence, 2022 | 10 | 2022 |
| Sequential Monte Carlo steering of large language models using probabilistic programs AK Lew, T Zhi-Xuan, G Grand, VK Mansinghka arXiv preprint arXiv:2306.03081, 2023 | 9 | 2023 |
| Bayesian causal inference via probabilistic program synthesis S Witty*, AK Lew*, D Jensen, V Mansinghka arXiv preprint arXiv:1910.14124, 2019 | 9 | 2019 |
| Leveraging unstructured statistical knowledge in a probabilistic language of thought AK Lew, MH Tessler, VK Mansinghka, JB Tenenbaum Proceedings of the Annual Conference of the Cognitive Science Society, 2020 | 8 | 2020 |
| PAP spaces: Reasoning denotationally about higher-order, recursive probabilistic and differentiable programs M Huot*, AK Lew*, VK Mansinghka, S Staton Logic in Computer Science (LICS 2023), 2023 | 6 | 2023 |
| Towards denotational semantics of AD for higher-order, recursive, probabilistic languages AK Lew, M Huot, VK Mansinghka NeurIPS Differentiable Programming Workshop (2021), 2021 | 6* | 2021 |
| Transforming worlds: automated involutive MCMC for open-universe probabilistic models G Matheos*, AK Lew*, M Ghavamizadeh, S Russell, ... Advances in Approximate Bayesian Inference, 2021 | 3 | 2021 |
| Differentiating Metropolis-Hastings to optimize intractable densities G Arya, R Seyer, F Schäfer, AK Lew, M Huot, VK Mansinghka, ... Differentiable Almost Everything (ICML 2023 workshop), 2023 | 2 | 2023 |
| Probabilistic programming with stochastic probabilities AK Lew, M Ghavamizadeh, MC Rinard, VK Mansinghka Proceedings of the ACM on Programming Languages 7 (PLDI), 1708-1732, 2023 | 2 | 2023 |
| What do posterior distributions of probabilistic programs look like? M Huot*, AK Lew*, V Mansinghka, S Staton Languages for Inference (LAFI), 2023 | 1 | 2023 |
Vikash K. MansinghkaMIT, Probabilistic Computing ProjectVerified email at mit.edu
