Alexander K. Lew
Alexander K. Lew
Graduate student, MIT
Verified email at - Homepage
Cited by
Cited by
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
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
Few-shot Bayesian imitation learning with logical program policies
T Silver, KR Allen, AK Lew, LP Kaelbling, J Tenenbaum
AAAI, 10251-10258, 2020
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
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
Automating involutive MCMC using probabilistic and differentiable programming
M Cusumano-Towner, AK Lew, VK Mansinghka
arXiv preprint arXiv:2007.09871, 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
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
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
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
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
Bayesian causal inference via probabilistic program synthesis
S Witty*, AK Lew*, D Jensen, V Mansinghka
arXiv preprint arXiv:1910.14124, 2019
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
Towards denotational semantics of AD for higher-order, recursive, probabilistic languages
AK Lew, M Huot, VK Mansinghka
NeurIPS Differentiable Programming Workshop (2021), 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
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
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
What do posterior distributions of probabilistic programs look like?
M Huot*, AK Lew*, V Mansinghka, S Staton
Languages for Inference (LAFI), 2023
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