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When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision.
Decidability and undecidability results for planning with numerical state variables
Malte Helmert · 2002
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Combinatorial sketching for finite programs
Armando Solar-Lezama, Liviu Tancau, Rastislav Bodik, Sanjit Seshia, and Vijay Saraswat · 2006
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Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Synthesis of loop-free programs
Sumit Gulwani, Susmit Jha, Ashish Tiwari, and Ramarathnam Venkatesan · 2011
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Colin: Planning with continuous linear numeric change
Amanda Jane Coles, Andrew I Coles, Maria Fox, and Derek Long · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fannie Mae single-family loan performance data, 2014
Fannie Mae · 2014
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Yi Li, Aws Albarghouthi, Zachary Kincaid, Arie Gurfinkel, and Marsha Chechik · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Neural programmer-interpreters
Scott Reed and Nando De Freitas · 2015
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SMT-based nonlinear PDDL+ planning
Daniel Bryce, Sicun Gao, David Musliner, and Robert Goldman · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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Terpret: A probabilistic programming language for program induction
Alexander L Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow · 2016
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A compilation of the full PDDL+ language into SMT
Michael Cashmore, Maria Fox, Derek Long, and Daniele Magazzeni · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Programming with a differentiable forth interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, and Sebastian Riedel · 2017
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Differentiable programs with neural libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, and Daniel Tarlow · 2017
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Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2017
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Scalable planning with tensorflow for hybrid nonlinear domains
Ga Wu, Buser Say, and Scott Sanner · 2017
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Search-based program synthesis
Rajeev Alur, Rishabh Singh, Dana Fisman, and Armando Solar-Lezama · 2018
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Heuristic planning for hybrid systems
Wiktor Mateusz Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni, and Fabio Mercorio · 2016
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Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters · 2016
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Program synthesis
Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Interpreting neural network judgments via minimal, stable, and symbolic corrections
Xin Zhang, Armando Solar-Lezama, and Rishabh Singh · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Houdini: Lifelong learning as program synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, and Swarat Chaudhuri · 2018
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Leveraging grammar and reinforcement learning for neural program synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli · 2018
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Scottyactivity: mixed discrete-continuous planning with convex optimization
Enrique Fernández-González, Brian Williams, and Erez Karpas · 2018
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song · 2019
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