Feng, Yu, Osbert Bastani, Ruben Martins, Isil Dillig, and Saswat Anand. ‘Automated Synthesis of Semantic Malware Signatures Using Maximum Satisfiability’. In NDSS, 2017. Gottschlich, Justin, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, and Tim Mattson. ‘The Three Pillars of Machine Programming’. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, MAPL-PLDI 2018, Philadelphia, PA, USA, June 18-22, 2018, 69–80, 2018. Ellis, Kevin, Adam Albright, Armando Solar-Lezama, Joshua B. Tenenbaum, and Timothy J. O’Donnell. ‘Synthesizing Theories of Human Language with Bayesian Program Induction’. Nature Communications 13, no. 5024 (2022). Polikarpova, Nadia, Ivan Kuraj, and Armando Solar-Lezama. ‘Program Synthesis from Polymorphic Refinement Types’. SIGPLAN Not. 51, no. 6 (June 2016): 522–38. Murali, Vijayaraghavan, Swarat Chaudhuri, and Chris Jermaine. ‘Bayesian Sketch Learning for Program Synthesis’. CoRR abs/1703.05698 (2017). Ellis, Kevin, Catherine Wong, Maxwell I. Nye, Mathias Sablé-Meyer, Lucas Morales, Luke B. Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B. Tenenbaum. ‘DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning’. In PLDI ’21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Virtual Event, Canada, June 20-25, 2021, edited by Stephen N. Freund and Eran Yahav, 835–50. ACM, 2021. Chaudhuri, Swarat, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, and Yisong Yue. ‘Neurosymbolic Programming’. Found. Trends Program. Lang. 7, no. 3 (2021): 158–243. Feser, John, Isil Dillig, and Armando Solar-Lezama. ‘Metric Program Synthesis for Inverse CSG’, 2022. Yang, Yichen, Jeevana Priya Inala, Osbert Bastani, Yewen Pu, Armando Solar-Lezama, and Martin Rinard. ‘Program Synthesis Guided Reinforcement Learning’, 2021. Das, Ria, Joshua B. Tenenbaum, Armando Solar-Lezama, and Zenna Tavares. ‘Synthesis of Reactive Programs with Structured Latent State’. In Advances in Programming Languages and Neurosymbolic Systems (AIPLANS ’21), 2021. Ellis, Kevin, Armando Solar-Lezama, and Joshua B. Tenenbaum. ‘Unsupervised Learning by Program Synthesis’. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 973–81, 2015. http://papers.nips.cc/paper/5785-unsupervised-learning-by-program-synthesis. Solar-Lezama, Armando. ‘Program Sketching’. STTT 15, no. 5–6 (2013): 475–95. Inala, Jeevana Priya, Nadia Polikarpova, Xiaokang Qiu, Benjamin S. Lerner, and Armando Solar-Lezama. ‘Synthesis of Recursive ADT Transformations from Reusable Templates’. In Tools and Algorithms for the Construction and Analysis of Systems - 23rd International Conference, TACAS 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22-29, 2017, Proceedings, Part I, 247–63, 2017. Solar-Lezama, Armando, Liviu Tancau, Rastislav Bodík, Sanjit A. Seshia, and Vijay A. Saraswat. ‘Combinatorial Sketching for Finite Programs’. In Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2006, San Jose, CA, USA, October 21-25, 2006, 404–15, 2006. Poesia, Gabriel, Wenxin Dong, and Noah Goodman. ‘Contrastive Reinforcement Learning of Symbolic Reasoning Domains’. Advances in Neural Information Processing Systems 34 (2021). Poesia, Gabriel, and Noah Goodman. ‘Left to the Reader: Abstracting Solutions in Mathematical Reasoning’. CogSci 2022 34 (2021). Sun, Jennifer J., Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, and Pietro Perona. ‘Task Programming: Learning Data Efficient Behavior Representations’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2876–85, 2021. Mukherjee, Rohan, Yeming Wen, Dipak Chaudhari, Thomas Reps, Swarat Chaudhuri, and Christopher Jermaine. ‘Neural Program Generation Modulo Static Analysis’. In Advances in Neural Information Processing Systems, edited by M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan, 34:18984–96. Curran Associates, Inc., 2021. Yang, Chenxi, and Swarat Chaudhuri. ‘Safe Neurosymbolic Learning with Differentiable Symbolic Execution’. In International Conference on Learning Representations, 2022. Shah, Ameesh, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue, and Swarat Chaudhuri. ‘Learning Differentiable Programs with Admissible Neural Heuristics’. In Advances in Neural Information Processing Systems, edited by H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, 33:4940–52. Curran Associates, Inc., 2020. Chen, Qiaochu, Aaron Lamoreaux, Xinyu Wang, Greg Durrett, Osbert Bastani, and Isil Dillig. ‘Web Question Answering with Neurosymbolic Program Synthesis’. In Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 328–43, 2021. Anderson, Greg, Abhinav Verma, Isil Dillig, and Swarat Chaudhuri. ‘Neurosymbolic Reinforcement Learning with Formally Verified Exploration’. Advances in Neural Information Processing Systems 33 (2020): 6172–83. Tjandrasuwita, Megan, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, and Yisong Yue. ‘Interpreting Expert Annotation Differences in Animal Behavior’. ArXiv Preprint ArXiv:2106. 06114, 2021. Ganea, Octavian-Eugen, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause. ‘Independent Se (3)-Equivariant Models for End-to-End Rigid Protein Docking’. ArXiv Preprint ArXiv:2111. 07786, 2021. Stärk, Hannes, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. ‘Equibind: Geometric Deep Learning for Drug Binding Structure Prediction’. In International Conference on Machine Learning, 20503–21. PMLR, 2022. Jing, Bowen, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola. ‘Torsional Diffusion for Molecular Conformer Generation’. ArXiv Preprint ArXiv:2206. 01729, 2022. Xie, Tian, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola. ‘Crystal Diffusion Variational Autoencoder for Periodic Material Generation’. ArXiv Preprint ArXiv:2110. 06197, 2021. Trippe, Brian L., Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola. ‘Diffusion Probabilistic Modeling of Protein Backbones in 3D for the Motif-Scaffolding Problem’. ArXiv Preprint ArXiv:2206. 04119, 2022. Yang, Yichen, Jeevana Priya Inala, Osbert Bastani, Yewen Pu, Armando Solar-Lezama, and Martin Rinard. "Program synthesis guided reinforcement learning for partially observed environments." Advances in neural information processing systems 34 (2021): 29669-29683. Inala, Jeevana Priya, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin Rinard, and Armando Solar-Lezama. ‘Neurosymbolic Transformers for Multi-Agent Communication’. Advances in Neural Information Processing Systems 33 (2020): 13597–608. Poesia, Gabriel, and Noah Goodman. "Pragmatic Code Autocomplete." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 1, pp. 445-452. 2021. Renda, Alex, Yishen Chen, Charith Mendis, and Michael Carbin. ‘Difftune: Optimizing Cpu Simulator Parameters with Learned Differentiable Surrogates’. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 442–55. IEEE, 2020. Zhan, Eric, Jennifer J. Sun, Ann Kennedy, Yisong Yue, and Swarat Chaudhuri. ‘Unsupervised Learning of Neurosymbolic Encoders’. Chowdhury, Arkabandhu, Mingchao Jiang, Swarat Chaudhuri, and Chris Jermaine. ‘Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier’. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9445–54, 2021. Wong, Catherine, Kevin M. Ellis, Joshua Tenenbaum, and Jacob Andreas. ‘Leveraging Language to Learn Program Abstractions and Search Heuristics’. In International Conference on Machine Learning, 11193–204. PMLR, 2021. Tseng, Albert, Jennifer J. Sun, and Yisong Yue. ‘Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2211–20, 2022. Sun, Jennifer J., Serim Ryou, Roni H. Goldshmid, Brandon Weissbourd, John O. Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, and Pietro Perona. ‘Self-Supervised Keypoint Discovery in Behavioral Videos’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2171–80, 2022. Sun, Jennifer J., Tomomi Karigo, Dipam Chakraborty, Sharada P. Mohanty, Benjamin Wild, Quan Sun, Chen Chen, et al. ‘The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions’. ArXiv Preprint ArXiv:2104. 02710, 2021. Sun, Jennifer J., Andrew Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, et al. ‘The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior’. ArXiv Preprint ArXiv:2207. 10553, 2022. Shah, Ameesh, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue, and Swarat Chaudhuri. "Learning differentiable programs with admissible neural heuristics." Advances in neural information processing systems 33 (2020): 4940-4952. Anderson, Greg, Abhinav Verma, Isil Dillig, and Swarat Chaudhuri. "Neurosymbolic reinforcement learning with formally verified exploration." Advances in neural information processing systems 33 (2020): 6172-6183. Mariano, Benjamin, Yanju Chen, Yu Feng, Greg Durrett, and Işil Dillig. "Automated transpilation of imperative to functional code using neural-guided program synthesis." Proceedings of the ACM on Programming Languages 6, no. OOPSLA1 (2022): 1-27.