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Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains.
Extended tables of the exact distribution of a rank statistic for all treatments multiple comparisons in one-way layout designs
Joseph A Damico and Douglas A Wolfe · 1987
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Approximation by superpositions of a sigmoidal function
George V. Cybenko · 1989
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Rule-based machine learning methods for functional prediction
Sholom M. Weiss and Nitin Indurkhya · 1995
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Globally optimal fuzzy decision trees for classification and regression
Alberto Suárez and James F Lutsko · 1999
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Neural-network approximation of piecewise continuous functions: application to friction compensation
Rastko R. Selmic and Frank L. Lewis · 2002
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A complete fuzzy decision tree technique
C Olaru and L Wehenkel · 2003
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Online adaptive decision trees
J Basak · 2004
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Classification and regression trees
Wei-Yin Loh · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, D. Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Convolutional decision trees for feature learning and segmentation
Dmitry Laptev and Joachim M Buhmann · 2014
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Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Christos Katrakazas, Mohammed A. Quddus, Wen‐Hua Chen, and Lipika Deka · 2015
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Interactive and interpretable machine learning models for human machine collaboration
Been Kim · 2015
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Deep neural decision forests
Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulo · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, David Madigan, et al · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Manfred Otto Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Learning certifiably optimal rule lists for categorical data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2017
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An optimization approach to learning falling rule lists
Chaofan Chen and Cynthia Rudin · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey E. Hinton · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Shane Gu, and Ben Poole · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, and Aditya V. Nori · 2018
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Beyond sparsity: Tree regularization of deep models for interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2018
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Introducing autonomous buses and taxis: Quantifying the potential benefits in japanese transportation systems
Ryosuke Abe · 2019
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Explainable machine learning in deployment, 2019
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley · 2019
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Robustness verification of tree-based models
Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, and Cho-Jui Hsieh · 2019
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Guy Katz, Clark W. Barrett, David L. Dill, Kyle D. Julian, and Mykel J. Kochenderfer · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
Jinkyu Kim and John F. Canny · 2017
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Introduction to Game Physics with Box2D
Ian Parberry · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Flow: Architecture and benchmarking for reinforcement learning in traffic control
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, and Alexandre M. Bayen · 2017
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Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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A convex relaxation barrier to tight robustness verification of neural networks
Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang · 2019
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Interpretability with accurate small models
Abhishek Ghose and Balaraman Ravindran · 2020
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Interpretable control by reinforcement learning
Daniel Hein, Steffen Limmer, and Thomas A. Runkler · 2020
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Interpretable and personalized apprenticeship scheduling: Learning interpretable scheduling policies from heterogeneous user demonstrations
Rohan Paleja, Andrew Silva, Letian Chen, and Matthew Gombolay · 2020
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The robotarium: Globally impactful opportunities, challenges, and lessons learned in remote-access, distributed control of multirobot systems
Sean Wilson, Paul Glotfelter, Li Wang, Siddharth Mayya, Gennaro Notomista, Mark L. Mote, and Magnus Egerstedt · 2020
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Regional tree regularization for interpretability in deep neural networks
Mike Wu, Sonali Parbhoo, Michael Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2020
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A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and K. Takeda · 2020
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Pytorch, explain! a python library for logic explained networks
Pietro Barbiero, Gabriele Ciravegna, Dobrik Georgiev, and Franscesco Giannini · 2021
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Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, M. Gori, Pietro Li’o, Marco Maggini, and S. Melacci · 2021
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Scalable multiagent driving policies for reducing traffic congestion
Jiaxun Cui, William Macke, Harel Yedidsion, Aastha Goyal, Daniel Urielli, and Peter Stone · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Highly accurate protein structure prediction with alphafold
John M Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and S. Kotsiantis · 2021
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The utility of explainable ai in ad hoc human-machine teaming
Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, and Matthew Gombolay · 2021
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Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong · 2021
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Encoding human domain knowledge to warm start reinforcement learning
Andrew Silva and Matthew Gombolay · 2021
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Natural language specification of reinforcement learning policies through differentiable decision trees
Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan, and Matthew C. Gombolay · 2023
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