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ORCIDBeen Kim
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- affiliation: Google, USA
- affiliation: AI2, Allen Institute for Artificial Intelligence, Seattle, US
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2020 – today
- 2024
[c34]Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim:
Don't trust your eyes: on the (un)reliability of feature visualizations. ICML 2024- 2023
- [j5]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) - [c33]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. ICML 2023: 15861-15883 - [c32]Devleena Das
, Been Kim, Sonia Chernova:
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. IUI 2023: 240-250 - [c31]Devleena Das, Sonia Chernova, Been Kim:
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. NeurIPS 2023 - [c30]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 - [c29]Zi Wang, Alexander Ku, Jason Baldridge, Tom Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. NeurIPS 2023 - [i45]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 abs/2301.04213 (2023) - [i44]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 abs/2305.15324 (2023) - [i43]Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR abs/2305.18213 (2023) - [i42]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 abs/2306.04719 (2023) - [i41]Devleena Das, Sonia Chernova, Been Kim:
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. CoRR abs/2309.12482 (2023) - [i40]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 abs/2310.13018 (2023) - [i39]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 abs/2310.16410 (2023) - 2022
- [c28]Julius Adebayo, Michael Muelly, Harold Abelson, Been Kim:
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. ICLR 2022 - [c27]Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard:
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. ICLR 2022 - [c26]Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim:
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. NeurIPS 2022 - [i38]Devleena Das, Been Kim, Sonia Chernova:
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. CoRR abs/2201.04204 (2022) - [i37]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. CoRR abs/2202.12451 (2022) - [i36]Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim:
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. CoRR abs/2206.09046 (2022) - [i35]Julius Adebayo, Michael Muelly, Hal Abelson, Been Kim:
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. CoRR abs/2212.04629 (2022) - [i34]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. CoRR abs/2212.06925 (2022) - [i33]Blair L. Bilodeau, Natasha Jaques, Pang Wei Koh, Been Kim:
Impossibility Theorems for Feature Attribution. CoRR abs/2212.11870 (2022) - 2021
- [j4]Been Kim, Finale Doshi-Velez:
Machine Learning Techniques for Accountability. AI Mag. 42(1): 47-52 (2021) - [j3]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: 108102 (2021) - [p2]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. Neuro-Symbolic Artificial Intelligence 2021: 337-352 - [i32]Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard:
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. CoRR abs/2105.15164 (2021) - [i31]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 abs/2106.08641 (2021) - [i30]Thomas McGrath, Andrei Kapishnikov, Nenad Tomasev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik:
Acquisition of Chess Knowledge in AlphaZero. CoRR abs/2111.09259 (2021) - [i29]Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas:
Analyzing a Caching Model. CoRR abs/2112.06989 (2021) - 2020
- [c25]Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang:
Concept Bottleneck Models. ICML 2020: 5338-5348 - [c24]Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim:
Debugging Tests for Model Explanations. NeurIPS 2020 - [c23]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 - [i28]Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang:
Concept Bottleneck Models. CoRR abs/2007.04612 (2020) - [i27]Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim:
Debugging Tests for Model Explanations. CoRR abs/2011.05429 (2020)
2010 – 2019
- 2019
- [c22]Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo:
Interpreting Black Box Predictions using Fisher Kernels. AISTATS 2019: 3382-3390 - [c21]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: 4 - [c20]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: 59-67 - [c19]Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, Been Kim:
Visualizing and Measuring the Geometry of BERT. NeurIPS 2019: 8592-8600 - [c18]Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim:
Towards Automatic Concept-based Explanations. NeurIPS 2019: 9273-9282 - [c17]Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim:
A Benchmark for Interpretability Methods in Deep Neural Networks. NeurIPS 2019: 9734-9745 - [p1]Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim:
The (Un)reliability of Saliency Methods. Explainable AI 2019: 267-280 - [i26]Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez:
An Evaluation of the Human-Interpretability of Explanation. CoRR abs/1902.00006 (2019) - [i25]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 abs/1902.02960 (2019) - [i24]Amirata Ghorbani, James Wexler, Been Kim:
Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. CoRR abs/1902.03129 (2019) - [i23]Been Kim, Emily Reif, Martin Wattenberg, Samy Bengio:
Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. CoRR abs/1903.01069 (2019) - [i22]Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda B. Viégas, Martin Wattenberg:
Visualizing and Measuring the Geometry of BERT. CoRR abs/1906.02715 (2019) - [i21]Yash Goyal, Uri Shalit, Been Kim:
Explaining Classifiers with Causal Concept Effect (CaCE). CoRR abs/1907.07165 (2019) - [i20]Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, Joydeep Ghosh:
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. CoRR abs/1907.09615 (2019) - [i19]Mengjiao Yang, Been Kim:
BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. CoRR abs/1907.09701 (2019) - [i18]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - 2018
- [c16]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) 2018 - [c15]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 - [c14]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: 2673-2682 - [c13]Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. NeurIPS 2018: 5546-5557 - [c12]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. NeurIPS 2018: 9525-9536 - [c11]Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. NeurIPS 2018: 10180-10189 - [i17]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 abs/1802.00682 (2018) - [i16]Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. CoRR abs/1805.11571 (2018) - [i15]Heinrich Jiang, Been Kim, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. CoRR abs/1805.11783 (2018) - [i14]Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh:
xGEMs: Generating Examplars to Explain Black-Box Models. CoRR abs/1806.08867 (2018) - [i13]Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim:
Evaluating Feature Importance Estimates. CoRR abs/1806.10758 (2018) - [i12]Been Kim, Kush R. Varshney, Adrian Weller:
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). CoRR abs/1807.01308 (2018) - [i11]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. CoRR abs/1810.03292 (2018) - [i10]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR abs/1810.03307 (2018) - [i9]Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo:
Interpreting Black Box Predictions using Fisher Kernels. CoRR abs/1810.10118 (2018) - 2017
- [c10]Nan-Chen Chen, Been Kim:
QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations. VAST 2017: 48-58 - [i8]Finale Doshi-Velez, Been Kim:
A Roadmap for a Rigorous Science of Interpretability. CoRR abs/1702.08608 (2017) - [i7]Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, Martin Wattenberg:
SmoothGrad: removing noise by adding noise. CoRR abs/1706.03825 (2017) - [i6]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 abs/1708.02666 (2017) - [i5]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 abs/1711.00867 (2017) - 2016
- [c9]Been Kim, Oluwasanmi Koyejo, Rajiv Khanna:
Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS 2016: 2280-2288 - [i4]Been Kim, Dmitry M. Malioutov, Kush R. Varshney:
Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). CoRR abs/1607.02531 (2016) - 2015
- [j2]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: 361-398 (2015) - [c8]Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie A. Shah:
Scalable and Interpretable Data Representation for High-Dimensional, Complex Data. AAAI 2015: 1763-1769 - [c7]Been Kim, Julie A. Shah, Finale Doshi-Velez:
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. NIPS 2015: 2260-2268 - [i3]Been Kim, Cynthia Rudin, Julie A. Shah:
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. CoRR abs/1503.01161 (2015) - 2014
- [j1]Been Kim, Cynthia Rudin:
Learning about meetings. Data Min. Knowl. Discov. 28(5-6): 1134-1157 (2014) - [c6]Been Kim, Cynthia Rudin, Julie A. Shah:
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS 2014: 1952-1960 - 2013
- [c5]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: 1394-1400 - [c4]Been Kim, Cynthia Rudin:
Machine Learning for Meeting Analysis. AAAI (Late-Breaking Developments) 2013 - [c3]Been Kim, Larry A. M. Bush, Julie Shah:
Quantitative estimation of the strength of agreements in goal-oriented meetings. CogSIMA 2013: 38-44 - [i2]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 abs/1306.0963 (2013) - [i1]Been Kim, Cynthia Rudin:
Learning About Meetings. CoRR abs/1306.1927 (2013) - 2012
- [c2]Julie A. Shah, Been Kim, Stefanos Nikolaidis:
Human-Inspired Techniques for Human-Machine Team Planning. AAAI Fall Symposium: Human Control of Bioinspired Swarms 2012 - 2010
- [c1]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: 3185-3192
Coauthor Index
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