Been Kim

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 70

Top Topics

Visual Concepts

Top Venues

Publications

Devleena Das, Sonia Chernova, Been Kim
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. NeurIPS (2023)
Mateo Espinosa Zarlenga, Zohreh Shams, Michael E. Nelson, Been Kim, Mateja Jamnik
TabCBM: Concept-based Interpretable Neural Networks for Tabular Data. Trans. Mach. Learn. Res. 2023 (2023)
Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR (2023)
Devleena Das, Sonia Chernova, Been Kim
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. CoRR (2023)
Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim
Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero. CoRR (2023)
Zi Wang, Alexander Ku, Jason Baldridge, Tom Griffiths, Been Kim
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. NeurIPS (2023)
Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, Mary Phuong, Jess Whittlestone, Jade Leung, Daniel Kokotajlo, Nahema Marchal, Markus Anderljung, Noam Kolt, Lewis Ho, Divya Siddarth, Shahar Avin, Will Hawkins, Been Kim, Iason Gabriel, Vijay Bolina, Jack Clark, Yoshua Bengio, Paul F. Christiano, Allan Dafoe
Model evaluation for extreme risks. CoRR (2023)
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths
Getting aligned on representational alignment. CoRR (2023)
Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
Don't trust your eyes: on the (un)reliability of feature visualizations. CoRR (2023)
Devleena Das, Been Kim, Sonia Chernova
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. IUI (2023)
Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. CoRR (2023)
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
On the Relationship Between Explanation and Prediction: A Causal View. ICML (2023)
Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. NeurIPS (2023)
Julius Adebayo, Michael Muelly, Hal Abelson, Been Kim
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. CoRR (2022)
Julius Adebayo, Michael Muelly, Harold Abelson, Been Kim
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. ICLR (2022)
Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. CoRR (2022)
Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
Human-Centered Concept Explanations for Neural Networks. CoRR (2022)
Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. NeurIPS (2022)
Blair Bilodeau, Natasha Jaques, Pang Wei Koh, Been Kim
Impossibility Theorems for Feature Attribution. CoRR (2022)
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. ICLR (2022)
Devleena Das, Been Kim, Sonia Chernova
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. CoRR (2022)
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
On the Relationship Between Explanation and Prediction: A Causal View. CoRR (2022)
Xiao Bai, Xiang Wang, Xianglong Liu, Qiang Liu, Jingkuan Song, Nicu Sebe, Been Kim
Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognit. 120 (2021)
Thomas McGrath, Andrei Kapishnikov, Nenad Tomasev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik
Acquisition of Chess Knowledge in AlphaZero. CoRR (2021)
Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas
Analyzing a Caching Model. CoRR (2021)
Been Kim, Finale Doshi-Velez
Machine Learning Techniques for Accountability. AI Mag. 42 (1) (2021)
Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, Been Kim
Best of both worlds: local and global explanations with human-understandable concepts. CoRR (2021)
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. CoRR (2021)
Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
Debugging Tests for Model Explanations. NeurIPS (2020)
Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS (2020)
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
Concept Bottleneck Models. CoRR (2020)
Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
Debugging Tests for Model Explanations. CoRR (2020)
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
Concept Bottleneck Models. ICML (2020)
Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister
On Concept-Based Explanations in Deep Neural Networks. CoRR (2019)
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Human Evaluation of Models Built for Interpretability. HCOMP (2019)
Yash Goyal, Uri Shalit, Been Kim
Explaining Classifiers with Causal Concept Effect (CaCE). CoRR (2019)
Been Kim, Emily Reif, Martin Wattenberg, Samy Bengio
Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. CoRR (2019)
Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim
Towards Automatic Concept-based Explanations. NeurIPS (2019)
Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, Been Kim
Visualizing and Measuring the Geometry of BERT. NeurIPS (2019)
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, Joydeep Ghosh
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. CoRR (2019)
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason D. Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda B. Viégas, Gregory S. Corrado, Martin C. Stumpe, Michael Terry
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making. CoRR (2019)
Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda B. Viégas, Martin Wattenberg
Visualizing and Measuring the Geometry of BERT. CoRR (2019)
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason D. Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda B. Viégas, Gregory S. Corrado, Martin C. Stumpe, Michael Terry
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. CHI (2019)
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez
An Evaluation of the Human-Interpretability of Explanation. CoRR (2019)
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
A Benchmark for Interpretability Methods in Deep Neural Networks. NeurIPS (2019)
Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo
Interpreting Black Box Predictions using Fisher Kernels. AISTATS (2019)
Mengjiao Yang, Been Kim
BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. CoRR (2019)
Amirata Ghorbani, James Wexler, Been Kim
Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. CoRR (2019)
Been Kim, Kush R. Varshney, Adrian Weller
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). CoRR (2018)
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. CoRR (2018)
Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta
To Trust Or Not To Trust A Classifier. NeurIPS (2018)
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne
Learning how to explain neural networks: PatternNet and PatternAttribution. ICLR (Poster) (2018)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML (2018)
Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. NeurIPS (2018)
Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, Finale Doshi-Velez
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. CoRR (2018)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. NeurIPS (2018)
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh
xGEMs: Generating Examplars to Explain Black-Box Models. CoRR (2018)
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
Evaluating Feature Importance Estimates. CoRR (2018)
Heinrich Jiang, Been Kim, Maya R. Gupta
To Trust Or Not To Trust A Classifier. CoRR (2018)
Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
Interpreting Black Box Predictions using Fisher Kernels. CoRR (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR (2018)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. CoRR (2018)
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, Martin Wattenberg
SmoothGrad: removing noise by adding noise. CoRR (2017)
Nan-Chen Chen, Been Kim
QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations. VAST (2017)
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
The (Un)reliability of saliency methods. CoRR (2017)
Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller
Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017). CoRR (2017)
Finale Doshi-Velez, Been Kim
A Roadmap for a Rigorous Science of Interpretability. CoRR (2017)
Been Kim, Oluwasanmi Koyejo, Rajiv Khanna
Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS (2016)
Been Kim, Dmitry M. Malioutov, Kush R. Varshney
Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). CoRR (2016)
Been Kim, Caleb M. Chacha, Julie A. Shah
Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior. J. Artif. Intell. Res. 52 (2015)
Been Kim, Cynthia Rudin, Julie A. Shah
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. CoRR (2015)
Been Kim, Julie A. Shah, Finale Doshi-Velez
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. NIPS (2015)
Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie A. Shah
Scalable and Interpretable Data Representation for High-Dimensional, Complex Data. AAAI (2015)
Been Kim, Cynthia Rudin, Julie A. Shah
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS (2014)
Been Kim, Cynthia Rudin
Learning about meetings. Data Min. Knowl. Discov. 28 (5-6) (2014)
Been Kim, Caleb M. Chacha, Julie A. Shah
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. AAAI (2013)
Been Kim, Larry A. M. Bush, Julie Shah
Quantitative estimation of the strength of agreements in goal-oriented meetings. CogSIMA (2013)
Been Kim, Cynthia Rudin
Machine Learning for Meeting Analysis. AAAI (Late-Breaking Developments) (2013)
Been Kim, Cynthia Rudin
Learning About Meetings. CoRR (2013)
Been Kim, Caleb M. Chacha, Julie A. Shah
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. CoRR (2013)
Julie A. Shah, Been Kim, Stefanos Nikolaidis
Human-Inspired Techniques for Human-Machine Team Planning. AAAI Fall Symposium: Human Control of Bioinspired Swarms (2012)
Been Kim, Michael Kaess, Luke Fletcher, John J. Leonard, Abraham Bachrach, Nicholas Roy, Seth J. Teller
Multiple relative pose graphs for robust cooperative mapping. ICRA (2010)