PyMC: a modern, and comprehensive probabilistic programming framework in Python von Abril-Pla, Oriol, Andreani, Virgile, Carroll, Colin, Dong, Larry, Fonnesbeck, Christopher J, Kochurov, Maxim, Kumar, Ravin, Lao, Junpeng, Luhmann, Christian C, Martin, Osvaldo A, Osthege, Michael, Vieira, Ricardo, Wiecki, Thomas, Zinkov, Robert

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Probabilistic Inference by Program Transformation in Hakaru (System Description) von Narayanan, Praveen, Carette, Jacques, Romano, Wren, Shan, Chung-chieh, Zinkov, Robert

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Mask wearing in community settings reduces SARS-CoV-2 transmission von Leech, Gavin, Rogers-Smith, Charlie, Monrad, Joshua Teperowski, Sandbrink, Jonas B., Snodin, Benedict, Zinkov, Robert, Rader, Benjamin, Brownstein, John S., Gal, Yarin, Bhatt, Samir, Sharma, Mrinank, Mindermann, Sören, Brauner, Jan M., Aitchison, Laurence

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Mask wearing in community settings reduces SARS-CoV-2 transmission von Leech, Gavin, Rogers-Smith, Charlie, Monrad, Joshua Teperowski, Sandbrink, Jonas B, Snodin, Benedict, Zinkov, Robert, Rader, Benjamin, Brownstein, John S, Gal, Yarin, Bhatt, Samir, Sharma, Mrinank, Mindermann, Sören, Brauner, Jan M, Aitchison, Laurence

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VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search von Brandfonbrener, David, Henniger, Simon, Raja, Sibi, Prasad, Tarun, Loughridge, Chloe, Cassano, Federico, Hu, Sabrina Ruixin, Yang, Jianang, Byrd, William E, Zinkov, Robert, Amin, Nada

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Composing inference algorithms as program transformations von Zinkov, Robert, Shan, Chung-chieh

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Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search von Brandfonbrener, David, Sibi Raja, Prasad, Tarun, Loughridge, Chloe, Yang, Jianang, Henniger, Simon, Byrd, William E, Zinkov, Robert, Amin, Nada

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Using Synthetic Data to Train Neural Networks is Model-Based Reasoning von Le, Tuan Anh, Baydin, Atilim Gunes, Zinkov, Robert, Wood, Frank

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Composing inference algorithms as program transformations von Zinkov, Robert, Chung-chieh Shan

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Faithful Inversion of Generative Models for Effective Amortized Inference von Webb, Stefan, Golinski, Adam, Zinkov, Robert, Siddharth, N, Rainforth, Tom, Teh, Yee Whye, Wood, Frank

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Amortized Rejection Sampling in Universal Probabilistic Programming von Naderiparizi, Saeid, Ścibior, Adam, Munk, Andreas, Ghadiri, Mehrdad, Baydin, Atılım Güneş, Gram-Hansen, Bradley, de Witt, Christian Schroeder, Zinkov, Robert, Torr, Philip H. S, Rainforth, Tom, Teh, Yee Whye, Wood, Frank

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Using Synthetic Data to Train Neural Networks is Model-Based Reasoning von Le, Tuan Anh, Baydin, Atilim Gunes, Zinkov, Robert, Wood, Frank

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Faithful Inversion of Generative Models for Effective Amortized Inference von Webb, Stefan, Golinski, Adam, Zinkov, Robert, Siddharth, N, Rainforth, Tom, Teh, Yee Whye, Wood, Frank

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Amortized Rejection Sampling in Universal Probabilistic Programming von Naderiparizi, Saeid, Ścibior, Adam, Munk, Andreas, Ghadiri, Mehrdad, Atılım Güneş Baydin, Gram-Hansen, Bradley, Schroeder de Witt, Christian, Zinkov, Robert, Torr, Philip H S, Rainforth, Tom, Teh, Yee Whye, Wood, Frank

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