Been Kim - RankMe

101 papers A* 24A 2B 2Misc 1Journal 61Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Interpreting and Controlling Model Behavior via Constitutions for Atomic Concept Edits. CoRR Neha Kalibhat, Zi Wang, Prasoon Bajpai, Drew Proud, Wenjun Zeng, Been Kim, Mani Malek
2025	J	`jnl`	Because we have LLMs, we Can and Should Pursue Agentic Interpretability. CoRR Been Kim, John Hewitt, Neel Nanda, Noah Fiedel, Oyvind Tafjord
2025	J	`jnl`	Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models. CoRR Lauren Hyoseo Yoon, Yisong Yue, Been Kim
2025	J	`jnl`	Getting aligned on representational alignment. Trans. Mach. Learn. Res. Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Christopher J. Cueva, Erin Grant, Iris I. A. Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nathan Cloos, Nikolaus Kriegeskorte, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew Kyle Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths
2025	J	`jnl`	How many classes do we need to see for novel class discovery? CoRR Akanksha Sarkar, Been Kim, Jennifer J. Sun
2025	A*	`conf`	How new data permeates LLM knowledge and how to dilute it. ICLR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	How new data permeates LLM knowledge and how to dilute it. CoRR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	Neologism Learning for Controllability and Self-Verbalization. CoRR John Hewitt, Oyvind Tafjord, Robert Geirhos, Been Kim
2025	—	`conf`	Position: We Can't Understand AI Using our Existing Vocabulary. ICML (Position Papers) John Hewitt, Robert Geirhos, Been Kim
2025	A*	`conf`	Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty. ICML Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola, Been Kim, Zi Wang
2025	J	`jnl`	QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks? CoRR Belinda Z. Li, Been Kim, Zi Wang
2025	J	`jnl`	Video models are zero-shot learners and reasoners. CoRR Thaddäus Wiedemer, Yuxuan Li, Paul Vicol, Shixiang Shane Gu, Nick Matarese, Kevin Swersky, Been Kim, Priyank Jaini, Robert Geirhos
2025	J	`jnl`	We Can't Understand AI Using our Existing Vocabulary. CoRR John Hewitt, Robert Geirhos, Been Kim
2024	B	`conf`	Can Generative Multimodal Models Count to Ten? CogSci Sunayana Rane, Alexander Ku, Jason Baldridge, Ian Tenney, Tom Griffiths, Been Kim
2024	A*	`conf`	Don't trust your eyes: on the (un)reliability of feature visualizations. ICML Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
2024	J	`jnl`	Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty. CoRR Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola, Been Kim, Zi Wang
2023	J	`jnl`	Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero. CoRR Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim
2023	A*	`conf`	Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. NeurIPS Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
2023	J	`jnl`	Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. CoRR Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
2023	J	`jnl`	Don't trust your eyes: on the (un)reliability of feature visualizations. CoRR Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
2023	A*	`conf`	Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. NeurIPS Zi Wang, Alexander Ku, Jason Baldridge, Tom Griffiths, Been Kim
2023	J	`jnl`	Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim
2023	J	`jnl`	Getting aligned on representational alignment. CoRR 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
2023	J	`jnl`	Model evaluation for extreme risks. CoRR 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
2023	A*	`conf`	On the Relationship Between Explanation and Prediction: A Causal View. ICML Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
2023	A*	`conf`	State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. NeurIPS Devleena Das, Sonia Chernova, Been Kim
2023	J	`jnl`	State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. CoRR Devleena Das, Sonia Chernova, Been Kim
2023	A	`conf`	Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. IUI Devleena Das, Been Kim, Sonia Chernova
2023	J	`jnl`	TabCBM: Concept-based Interpretable Neural Networks for Tabular Data. Trans. Mach. Learn. Res. Mateo Espinosa Zarlenga, Zohreh Shams, Michael E. Nelson, Been Kim, Mateja Jamnik
2022	A*	`conf`	Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. NeurIPS Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim
2022	J	`jnl`	Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. CoRR Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim
2022	A*	`conf`	DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. ICLR Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
2022	J	`jnl`	Human-Centered Concept Explanations for Neural Networks. CoRR Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
2022	J	`jnl`	Impossibility Theorems for Feature Attribution. CoRR Blair L. Bilodeau, Natasha Jaques, Pang Wei Koh, Been Kim
2022	J	`jnl`	On the Relationship Between Explanation and Prediction: A Causal View. CoRR Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
2022	A*	`conf`	Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. ICLR Julius Adebayo, Michael Muelly, Harold Abelson, Been Kim
2022	J	`jnl`	Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. CoRR Julius Adebayo, Michael Muelly, Hal Abelson, Been Kim
2022	J	`jnl`	Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. CoRR Devleena Das, Been Kim, Sonia Chernova
2021	J	`jnl`	Acquisition of Chess Knowledge in AlphaZero. CoRR Thomas McGrath, Andrei Kapishnikov, Nenad Tomasev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik
2021	J	`jnl`	Analyzing a Caching Model. CoRR Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas
2021	J	`jnl`	Best of both worlds: local and global explanations with human-understandable concepts. CoRR Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, Been Kim
2021	J	`jnl`	DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. CoRR Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard
2021	J	`jnl`	Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognit. Xiao Bai, Xiang Wang, Xianglong Liu, Qiang Liu, Jingkuan Song, Nicu Sebe, Been Kim
2021	—	`ch.`	Human-Centered Concept Explanations for Neural Networks. Neuro-Symbolic Artificial Intelligence Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
2021	J	`jnl`	Machine Learning Techniques for Accountability. AI Mag. Been Kim, Finale Doshi-Velez
2020	A*	`conf`	Concept Bottleneck Models. ICML Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
2020	J	`jnl`	Concept Bottleneck Models. CoRR Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
2020	A*	`conf`	Debugging Tests for Model Explanations. NeurIPS Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
2020	J	`jnl`	Debugging Tests for Model Explanations. CoRR Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
2020	A*	`conf`	On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar
2019	A*	`conf`	A Benchmark for Interpretability Methods in Deep Neural Networks. NeurIPS Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
2019	J	`jnl`	An Evaluation of the Human-Interpretability of Explanation. CoRR Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez
2019	J	`jnl`	Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. CoRR Amirata Ghorbani, James Wexler, Been Kim
2019	J	`jnl`	BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. CoRR Mengjiao Yang, Been Kim
2019	J	`jnl`	Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. CoRR Been Kim, Emily Reif, Martin Wattenberg, Samy Bengio
2019	J	`jnl`	Explaining Classifiers with Causal Concept Effect (CaCE). CoRR Yash Goyal, Uri Shalit, Been Kim
2019	B	`conf`	Human Evaluation of Models Built for Interpretability. HCOMP Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
2019	A*	`conf`	Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. CHI 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
2019	J	`jnl`	Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making. CoRR 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
2019	A	`conf`	Interpreting Black Box Predictions using Fisher Kernels. AISTATS Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo
2019	J	`jnl`	On Concept-Based Explanations in Deep Neural Networks. CoRR Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister
2019	—	`ch.`	The (Un)reliability of Saliency Methods. Explainable AI Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
2019	A*	`conf`	Towards Automatic Concept-based Explanations. NeurIPS Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim
2019	J	`jnl`	Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. CoRR Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, Joydeep Ghosh
2019	A*	`conf`	Visualizing and Measuring the Geometry of BERT. NeurIPS Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, Been Kim
2019	J	`jnl`	Visualizing and Measuring the Geometry of BERT. CoRR Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda B. Viégas, Martin Wattenberg
2018	J	`jnl`	Evaluating Feature Importance Estimates. CoRR Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
2018	J	`jnl`	How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. CoRR Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, Finale Doshi-Velez
2018	A*	`conf`	Human-in-the-Loop Interpretability Prior. NeurIPS Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
2018	J	`jnl`	Human-in-the-Loop Interpretability Prior. CoRR Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
2018	A*	`conf`	Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres
2018	J	`jnl`	Interpreting Black Box Predictions using Fisher Kernels. CoRR Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
2018	—	`conf`	Learning how to explain neural networks: PatternNet and PatternAttribution. ICLR (Poster) Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne
2018	—	`conf`	Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
2018	J	`jnl`	Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
2018	J	`jnl`	Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). CoRR Been Kim, Kush R. Varshney, Adrian Weller
2018	A*	`conf`	Sanity Checks for Saliency Maps. NeurIPS Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
2018	J	`jnl`	Sanity Checks for Saliency Maps. CoRR Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
2018	A*	`conf`	To Trust Or Not To Trust A Classifier. NeurIPS Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta
2018	J	`jnl`	To Trust Or Not To Trust A Classifier. CoRR Heinrich Jiang, Been Kim, Maya R. Gupta
2018	J	`jnl`	xGEMs: Generating Examplars to Explain Black-Box Models. CoRR Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh
2017	J	`jnl`	A Roadmap for a Rigorous Science of Interpretability. CoRR Finale Doshi-Velez, Been Kim
2017	J	`jnl`	Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017). CoRR Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller
2017	—	`conf`	QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations. VAST Nan-Chen Chen, Been Kim
2017	J	`jnl`	SmoothGrad: removing noise by adding noise. CoRR Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, Martin Wattenberg
2017	J	`jnl`	The (Un)reliability of saliency methods. CoRR Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
2016	—	`conf`	Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS Been Kim, Oluwasanmi Koyejo, Rajiv Khanna
2016	J	`jnl`	Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). CoRR Been Kim, Dmitry M. Malioutov, Kush R. Varshney
2015	J	`jnl`	Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior. J. Artif. Intell. Res. Been Kim, Caleb M. Chacha, Julie A. Shah
2015	—	`conf`	Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. NIPS Been Kim, Julie A. Shah, Finale Doshi-Velez
2015	A*	`conf`	Scalable and Interpretable Data Representation for High-Dimensional, Complex Data. AAAI Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie A. Shah
2015	J	`jnl`	The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. CoRR Been Kim, Cynthia Rudin, Julie A. Shah
2014	J	`jnl`	Learning about meetings. Data Min. Knowl. Discov. Been Kim, Cynthia Rudin
2014	—	`conf`	The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS Been Kim, Cynthia Rudin, Julie A. Shah
2013	A*	`conf`	Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. AAAI Been Kim, Caleb M. Chacha, Julie A. Shah
2013	J	`jnl`	Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. CoRR Been Kim, Caleb M. Chacha, Julie A. Shah
2013	J	`jnl`	Learning About Meetings. CoRR Been Kim, Cynthia Rudin
2013	—	`conf`	Machine Learning for Meeting Analysis. AAAI (Late-Breaking Developments) Been Kim, Cynthia Rudin
2013	Misc	`conf`	Quantitative estimation of the strength of agreements in goal-oriented meetings. CogSIMA Been Kim, Larry A. M. Bush, Julie Shah
2012	—	`conf`	Human-Inspired Techniques for Human-Machine Team Planning. AAAI Fall Symposium: Human Control of Bioinspired Swarms Julie A. Shah, Been Kim, Stefanos Nikolaidis
2010	A*	`conf`	Multiple relative pose graphs for robust cooperative mapping. ICRA Been Kim, Michael Kaess, Luke Fletcher, John J. Leonard, Abraham Bachrach, Nicholas Roy, Seth J. Teller