Himabindu Lakkaraju

160 papers A* 34A 17B 2C 6Journal 91Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders. EACL (Volume 1: Long Papers) Aaron J. Li, Suraj Srinivas, Usha Bhalla, Himabindu Lakkaraju
2026	J	`jnl`	Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning. CoRR Zidi Xiong, Shan Chen, Himabindu Lakkaraju
2025	J	`jnl`	Accountability Attribution: Tracing Model Behavior to Training Processes. CoRR Shichang Zhang, Hongzhe Du, Karim Saraipour, Jiaqi W. Ma, Himabindu Lakkaraju
2025	A	`conf`	Counterfactual Explanations May Not Be the Best Algorithmic Recourse Approach. IUI Sohini Upadhyay, Himabindu Lakkaraju, Krzysztof Z. Gajos
2025	J	`jnl`	Detecting LLM-Written Peer Reviews. CoRR Vishisht Rao, Aounon Kumar, Himabindu Lakkaraju, Nihar B. Shah
2025	J	`jnl`	EvoLM: In Search of Lost Language Model Training Dynamics. CoRR Zhenting Qi, Fan Nie, Alexandre Alahi, James Zou, Himabindu Lakkaraju, Yilun Du, Eric P. Xing, Sham M. Kakade, Hanlin Zhang
2025	A*	`conf`	Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. ICLR Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
2025	J	`jnl`	Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models. CoRR Martin Pawelczyk, Lillian Sun, Zhenting Qi, Aounon Kumar, Himabindu Lakkaraju
2025	J	`jnl`	How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior. CoRR Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
2025	J	`jnl`	How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence. CoRR Hongzhe Du, Weikai Li, Min Cai, Karim Saraipour, Zimin Zhang, Himabindu Lakkaraju, Yizhou Sun, Shichang Zhang
2025	J	`jnl`	Inference-Time Reward Hacking in Large Language Models. CoRR Hadi Khalaf, Claudio Mayrink Verdun, Alex Oesterling, Himabindu Lakkaraju, Flávio du Pin Calmon
2025	J	`jnl`	Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations. CoRR Aaron J. Li, Suraj Srinivas, Usha Bhalla, Himabindu Lakkaraju
2025	J	`jnl`	Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models. CoRR Zidi Xiong, Shan Chen, Zhenting Qi, Himabindu Lakkaraju
2025	A*	`conf`	More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness. ICLR Aaron Jiaxun Li, Satyapriya Krishna, Himabindu Lakkaraju
2025	—	`conf`	On the Impact of Fine-Tuning on Chain-of-Thought Reasoning. NAACL (Long Papers) Elita A. Lobo, Chirag Agarwal, Himabindu Lakkaraju
2025	A*	`conf`	Quantifying Generalization Complexity for Large Language Models. ICLR Zhenting Qi, Hongyin Luo, Xuliang Huang, Zhuokai Zhao, Yibo Jiang, Xiangjun Fan, Himabindu Lakkaraju, James R. Glass
2025	B	`conf`	Soft Best-of-$n$ Sampling for Model Alignment. ISIT Claudio Mayrink Verdun, Alex Oesterling, Himabindu Lakkaraju, Flávio P. Calmon
2025	J	`jnl`	Soft Best-of-n Sampling for Model Alignment. CoRR Claudio Mayrink Verdun, Alex Oesterling, Himabindu Lakkaraju, Flávio P. Calmon
2025	J	`jnl`	Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability. CoRR Usha Bhalla, Alex Oesterling, Claudio Mayrink Verdun, Himabindu Lakkaraju, Flávio P. Calmon
2025	J	`jnl`	Towards Interpretable Soft Prompts. CoRR Oam Patel, Jason Wang, Nikhil Shivakumar Nayak, Suraj Srinivas, Himabindu Lakkaraju
2025	J	`jnl`	Towards Uncovering How Large Language ModelsWork: An Interpretability Perspective. SIGKDD Explor. Haiyan Zhao, Fan Yang, Bo Shen, Ali Payani, Himabindu Lakkaraju, Mengnan Du
2024	—	`conf`	A Study on the Calibration of In-context Learning. NAACL-HLT Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2024	J	`jnl`	All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. CoRR Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju
2024	A	`conf`	Characterizing Data Point Vulnerability as Average-Case Robustness. UAI Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
2024	—	`conf`	Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. NAACL-HLT Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
2024	J	`jnl`	Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
2024	J	`jnl`	Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. CoRR Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju
2024	A	`conf`	Fair Machine Unlearning: Data Removal while Mitigating Disparities. AISTATS Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Himabindu Lakkaraju
2024	J	`jnl`	Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models. CoRR Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju
2024	J	`jnl`	Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. CoRR Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
2024	J	`jnl`	Generalized Group Data Attribution. CoRR Dan Ley, Suraj Srinivas, Shichang Zhang, Gili Rusak, Himabindu Lakkaraju
2024	A*	`conf`	In-Context Unlearning: Language Models as Few-Shot Unlearners. ICML Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
2024	J	`jnl`	Interpretability Needs a New Paradigm. CoRR Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, Sarath Chandar
2024	A*	`conf`	Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). NeurIPS Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju
2024	J	`jnl`	Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). CoRR Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju
2024	J	`jnl`	Learning Recourse Costs from Pairwise Feature Comparisons. CoRR Kaivalya Rawal, Himabindu Lakkaraju
2024	J	`jnl`	Manipulating Large Language Models to Increase Product Visibility. CoRR Aounon Kumar, Himabindu Lakkaraju
2024	A*	`conf`	MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. NeurIPS Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness. CoRR Aaron Jiaxun Li, Satyapriya Krishna, Himabindu Lakkaraju
2024	J	`jnl`	On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. CoRR Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	On the Impact of Fine-Tuning on Chain-of-Thought Reasoning. CoRR Elita A. Lobo, Chirag Agarwal, Himabindu Lakkaraju
2024	—	`conf`	On the Trade-offs between Adversarial Robustness and Actionable Explanations. AIES (1) Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning. CoRR Jiaqi Ma, Vivian Lai, Yiming Zhang, Chacha Chen, Paul Hamilton, Davor Ljubenkov, Himabindu Lakkaraju, Chenhao Tan
2024	J	`jnl`	Opening the Black Box of Large Language Models: Two Views on Holistic Interpretability. CoRR Haiyan Zhao, Fan Yang, Himabindu Lakkaraju, Mengnan Du
2024	J	`jnl`	Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers. CoRR Alex Oesterling, Usha Bhalla, Suresh Venkatasubramanian, Himabindu Lakkaraju
2024	J	`jnl`	Quantifying Generalization Complexity for Large Language Models. CoRR Zhenting Qi, Hongyin Luo, Xuliang Huang, Zhuokai Zhao, Yibo Jiang, Xiangjun Fan, Himabindu Lakkaraju, James R. Glass
2024	A	`conf`	Quantifying Uncertainty in Natural Language Explanations of Large Language Models. AISTATS Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. Trans. Mach. Learn. Res. Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju
2024	A*	`conf`	The First Workshop on AI Behavioral Science. KDD Himabindu Lakkaraju, Qiaozhu Mei, Chenhao Tan, Jie Tang, Yutong Xie
2024	J	`jnl`	Towards Safe and Aligned Large Language Models for Medicine. CoRR Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	Towards Unifying Interpretability and Control: Evaluation via Intervention. CoRR Usha Bhalla, Suraj Srinivas, Asma Ghandeharioun, Himabindu Lakkaraju
2024	A*	`conf`	Understanding the Effects of Iterative Prompting on Truthfulness. ICML Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2024	J	`jnl`	Understanding the Effects of Iterative Prompting on Truthfulness. CoRR Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2023	J	`jnl`	A Study on the Calibration of In-context Learning. CoRR Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2023	J	`jnl`	Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage. CoRR Catherine Huang, Chelse Swoopes, Christina Xiao, Jiaqi Ma, Himabindu Lakkaraju
2023	J	`jnl`	Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions. CoRR Skyler Wu, Eric Meng Shen, Charumathi Badrinath, Jiaqi Ma, Himabindu Lakkaraju
2023	J	`jnl`	Are Large Language Models Post Hoc Explainers? CoRR Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2023	J	`jnl`	Consistent Explanations in the Face of Model Indeterminacy via Ensembling. CoRR Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju
2023	A*	`conf`	Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. NeurIPS Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
2023	J	`jnl`	Efficient Estimation of the Local Robustness of Machine Learning Models. CoRR Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
2023	J	`jnl`	Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat. Mac. Intell. Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
2023	A*	`conf`	Generative AI meets Responsible AI: Practical Challenges and Opportunities. KDD Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani
2023	J	`jnl`	In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
2023	J	`jnl`	Investigating the Fairness of Large Language Models for Predictions on Tabular Data. CoRR Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
2023	J	`jnl`	Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Experts. CoRR Tessa Han, Yasha Ektefaie, Maha R. Farhat, Marinka Zitnik, Himabindu Lakkaraju
2023	A*	`conf`	M NeurIPS Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong
2023	A	`conf`	On Minimizing the Impact of Dataset Shifts on Actionable Explanations. UAI Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
2023	J	`jnl`	On Minimizing the Impact of Dataset Shifts on Actionable Explanations. CoRR Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
2023	A*	`conf`	On the Impact of Algorithmic Recourse on Social Segregation. ICML Ruijiang Gao, Himabindu Lakkaraju
2023	A	`conf`	On the Privacy Risks of Algorithmic Recourse. AISTATS Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
2023	J	`jnl`	On the Trade-offs between Adversarial Robustness and Actionable Explanations. CoRR Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2023	A*	`conf`	Post Hoc Explanations of Language Models Can Improve Language Models. NeurIPS Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
2023	J	`jnl`	Post Hoc Explanations of Language Models Can Improve Language Models. CoRR Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
2023	A*	`conf`	Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. ICLR Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
2023	J	`jnl`	Quantifying Uncertainty in Natural Language Explanations of Large Language Models. CoRR Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
2023	A*	`conf`	Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. ICML Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju
2023	J	`jnl`	Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. CoRR Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju
2023	—	`conf`	Tutorials at The Web Conference 2023. WWW (Companion Volume) Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne R. Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu
2023	J	`jnl`	Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. CoRR Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
2023	J	`jnl`	When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. Trans. Mach. Learn. Res. Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju
2023	A*	`conf`	Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. NeurIPS Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju
2023	J	`jnl`	Word-Level Explanations for Analyzing Bias in Text-to-Image Models. CoRR Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju
2022	B	`conf`	A Human-Centric Perspective on Model Monitoring. HCOMP Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
2022	J	`jnl`	A Human-Centric Take on Model Monitoring. CoRR Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
2022	J	`jnl`	Algorithmic Recourse in the Face of Noisy Human Responses. CoRR Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
2022	A	`conf`	Data poisoning attacks on off-policy policy evaluation methods. UAI Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
2022	A*	`conf`	Efficient Training of Low-Curvature Neural Networks. NeurIPS Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
2022	J	`jnl`	Evaluating Explainability for Graph Neural Networks. CoRR Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik
2022	A	`conf`	Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju
2022	C	`conf`	Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. AIES Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju
2022	J	`jnl`	Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. CoRR Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju
2022	J	`jnl`	Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. CoRR Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
2022	A*	`conf`	Model Monitoring in Practice: Lessons Learned and Open Challenges. KDD Krishnaram Kenthapadi, Himabindu Lakkaraju, Pradeep Natarajan, Mehrnoosh Sameki
2022	J	`jnl`	On the Privacy Risks of Algorithmic Recourse. CoRR Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
2022	A*	`conf`	OpenXAI: Towards a Transparent Evaluation of Model Explanations. NeurIPS Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
2022	J	`jnl`	OpenXAI: Towards a Transparent Evaluation of Model Explanations. CoRR Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
2022	A	`conf`	Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. AISTATS Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
2022	J	`jnl`	Rethinking Explainability as a Dialogue: A Practitioner's Perspective. CoRR Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh
2022	J	`jnl`	Rethinking Stability for Attribution-based Explanations. CoRR Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju
2022	J	`jnl`	TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. CoRR Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
2022	J	`jnl`	The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. CoRR Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju
2022	C	`conf`	Towards Robust Off-Policy Evaluation via Human Inputs. AIES Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
2022	J	`jnl`	Towards Robust Off-Policy Evaluation via Human Inputs. CoRR Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
2022	A*	`conf`	Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. NeurIPS Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
2022	J	`jnl`	Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations. CoRR Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
2021	A*	`conf`	Counterfactual Explanations Can Be Manipulated. NeurIPS Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh
2021	J	`jnl`	Counterfactual Explanations Can Be Manipulated. CoRR Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh
2021	C	`conf`	Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring. AIES Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
2021	A*	`conf`	Fair Influence Maximization: a Welfare Optimization Approach. AAAI Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe
2021	J	`jnl`	Feature Attributions and Counterfactual Explanations Can Be Manipulated. CoRR Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
2021	A*	`conf`	Learning Models for Actionable Recourse. NeurIPS Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
2021	J	`jnl`	Learning Under Adversarial and Interventional Shifts. CoRR Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
2021	J	`jnl`	On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju
2021	A*	`conf`	Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. NeurIPS Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju
2021	A	`conf`	Towards Reliable and Practicable Algorithmic Recourse. CIKM Himabindu Lakkaraju
2021	A*	`conf`	Towards Robust and Reliable Algorithmic Recourse. NeurIPS Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
2021	J	`jnl`	Towards Robust and Reliable Algorithmic Recourse. CoRR Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
2021	J	`jnl`	Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations. CoRR Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
2021	J	`jnl`	Towards a Unified Framework for Fair and Stable Graph Representation Learning. CoRR Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik
2021	A	`conf`	Towards a unified framework for fair and stable graph representation learning. UAI Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik
2021	A*	`conf`	Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. ICML Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju
2021	J	`jnl`	Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju
2021	J	`jnl`	What will it take to generate fairness-preserving explanations? CoRR Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju
2020	C	`conf`	"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations. AIES Himabindu Lakkaraju, Osbert Bastani
2020	A*	`conf`	Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. NeurIPS Kaivalya Rawal, Himabindu Lakkaraju
2020	J	`jnl`	Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses. CoRR Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju
2020	J	`jnl`	Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring. CoRR Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
2020	J	`jnl`	Ensuring Actionable Recourse via Adversarial Training. CoRR Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
2020	J	`jnl`	Fair Influence Maximization: A Welfare Optimization Approach. CoRR Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe
2020	C	`conf`	Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. AIES Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
2020	J	`jnl`	How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. CoRR Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
2020	A*	`conf`	Incorporating Interpretable Output Constraints in Bayesian Neural Networks. NeurIPS Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez
2020	J	`jnl`	Incorporating Interpretable Output Constraints in Bayesian Neural Networks. CoRR Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez
2020	J	`jnl`	Interpretable and Interactive Summaries of Actionable Recourses. CoRR Kaivalya Rawal, Himabindu Lakkaraju
2020	A*	`conf`	Robust and Stable Black Box Explanations. ICML Himabindu Lakkaraju, Nino Arsov, Osbert Bastani
2020	J	`jnl`	Robust and Stable Black Box Explanations. CoRR Himabindu Lakkaraju, Nino Arsov, Osbert Bastani
2020	J	`jnl`	When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. CoRR Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju
2019	J	`jnl`	"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations. CoRR Himabindu Lakkaraju, Osbert Bastani
2019	C	`conf`	Faithful and Customizable Explanations of Black Box Models. AIES Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
2019	J	`jnl`	How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. CoRR Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
2018	—	`—`	Human-centric machine learning: enabling machine learning for high-stakes decision-making. Himabindu Lakkaraju
2017	A*	`conf`	Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. AAAI Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
2017	J	`jnl`	Interpretable & Explorable Approximations of Black Box Models. CoRR Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
2017	A	`conf`	Learning Cost-Effective and Interpretable Treatment Regimes. AISTATS Himabindu Lakkaraju, Cynthia Rudin
2017	A*	`conf`	The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables. KDD Himabindu Lakkaraju, Jon M. Kleinberg, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan
2016	—	`conf`	Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making. NIPS Himabindu Lakkaraju, Jure Leskovec
2016	J	`jnl`	Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. CoRR Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
2016	A*	`conf`	Interpretable Decision Sets: A Joint Framework for Description and Prediction. KDD Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec
2016	J	`jnl`	Learning Cost-Effective Treatment Regimes using Markov Decision Processes. CoRR Himabindu Lakkaraju, Cynthia Rudin
2016	J	`jnl`	Psycho-Demographic Analysis of the Facebook Rainbow Campaign. CoRR Yilun Wang, Himabindu Lakkaraju, Michal Kosinski, Jure Leskovec
2015	A	`conf`	A Bayesian Framework for Modeling Human Evaluations. SDM Himabindu Lakkaraju, Jure Leskovec, Jon M. Kleinberg, Sendhil Mullainathan
2015	A*	`conf`	A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes. KDD Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, Kecia L. Addison
2015	A	`conf`	Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time. LAK Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia L. Addison
2013	A	`conf`	What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media. ICWSM Himabindu Lakkaraju, Julian J. McAuley, Jure Leskovec
2012	J	`jnl`	Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media CoRR Himabindu Lakkaraju, Indrajit Bhattacharya, Chiranjib Bhattacharyya
2012	A*	`conf`	Dynamic Multi-relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media. ICDM Himabindu Lakkaraju, Indrajit Bhattacharya, Chiranjib Bhattacharyya
2012	—	`conf`	TEM: a novel perspective to modeling content onmicroblogs. WWW (Companion Volume) Himabindu Lakkaraju, Hyung-il Ahn
2011	A	`conf`	Attention prediction on social media brand pages. CIKM Himabindu Lakkaraju, Jitendra Ajmera
2011	A	`conf`	Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments. SDM Himabindu Lakkaraju, Chiranjib Bhattacharyya, Indrajit Bhattacharya, Srujana Merugu
2011	—	`conf`	Smart news feeds for social networks using scalable joint latent factor models. WWW (Companion Volume) Himabindu Lakkaraju, Angshu Rai, Srujana Merugu