Himabindu Lakkaraju

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 116

Top Venues

Publications

Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. NAACL-HLT (2024)
Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, Himabindu Lakkaraju
On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. CoRR (2024)
Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. Trans. Mach. Learn. Res. 2024 (2024)
Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models. CoRR (2024)
Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR (2024)
Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju
Towards Safe and Aligned Large Language Models for Medicine. CoRR (2024)
Alex Oesterling, Usha Bhalla, Suresh Venkatasubramanian, Himabindu Lakkaraju
Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers. CoRR (2024)
Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, Sarath Chandar
Interpretability Needs a New Paradigm. CoRR (2024)
Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Himabindu Lakkaraju
Fair Machine Unlearning: Data Removal while Mitigating Disparities. AISTATS (2024)
Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. CoRR (2024)
Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. CoRR (2024)
Haiyan Zhao, Fan Yang, Himabindu Lakkaraju, Mengnan Du
Opening the Black Box of Large Language Models: Two Views on Holistic Interpretability. CoRR (2024)
Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
Quantifying Uncertainty in Natural Language Explanations of Large Language Models. AISTATS (2024)
Aaron J. Li, Satyapriya Krishna, Himabindu Lakkaraju
More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness. CoRR (2024)
Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
A Study on the Calibration of In-context Learning. NAACL-HLT (2024)
Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). CoRR (2024)
Jiaqi Ma, Vivian Lai, Yiming Zhang, Chacha Chen, Paul Hamilton, Davor Ljubenkov, Himabindu Lakkaraju, Chenhao Tan
OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning. CoRR (2024)
Aounon Kumar, Himabindu Lakkaraju
Manipulating Large Language Models to Increase Product Visibility. CoRR (2024)
Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. CoRR (2024)
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
Understanding the Effects of Iterative Prompting on Truthfulness. CoRR (2024)
Himabindu Lakkaraju, Qiaozhu Mei, Chenhao Tan, Jie Tang, Yutong Xie
The First Workshop on AI Behavioral Science. KDD (2024)
Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
Are Large Language Models Post Hoc Explainers? CoRR (2023)
Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani
Generative AI meets Responsible AI: Practical Challenges and Opportunities. KDD (2023)
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 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
Tutorials at The Web Conference 2023. WWW (Companion Volume) (2023)
Tessa Han, Yasha Ektefaie, Maha Farhat, Marinka Zitnik, Himabindu Lakkaraju
Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Experts. CoRR (2023)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Efficient Estimation of the Local Robustness of Machine Learning Models. CoRR (2023)
Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. ICML (2023)
Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju
Word-Level Explanations for Analyzing Bias in Text-to-Image Models. CoRR (2023)
Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
Investigating the Fairness of Large Language Models for Predictions on Tabular Data. CoRR (2023)
Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. CoRR (2023)
Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. UAI (2023)
Ruijiang Gao, Himabindu Lakkaraju
On the Impact of Algorithmic Recourse on Social Segregation. ICML (2023)
Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong
: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models. NeurIPS (2023)
Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
Post Hoc Explanations of Language Models Can Improve Language Models. NeurIPS (2023)
Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju
Consistent Explanations in the Face of Model Indeterminacy via Ensembling. CoRR (2023)
Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
Quantifying Uncertainty in Natural Language Explanations of Large Language Models. CoRR (2023)
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
On the Trade-offs between Adversarial Robustness and Actionable Explanations. CoRR (2023)
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. NeurIPS (2023)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. AISTATS (2023)
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat. Mac. Intell. 5 (8) (2023)
Skyler Wu, Eric Meng Shen, Charumathi Badrinath, Jiaqi Ma, Himabindu Lakkaraju
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions. CoRR (2023)
Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. Trans. Mach. Learn. Res. 2023 (2023)
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR (2023)
Catherine Huang, Chelse Swoopes, Christina Xiao, Jiaqi Ma, Himabindu Lakkaraju
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage. CoRR (2023)
Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
Post Hoc Explanations of Language Models Can Improve Language Models. CoRR (2023)
Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. ICLR (2023)
Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. NeurIPS (2023)
Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. CoRR (2023)
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. CoRR (2023)
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
Towards Robust Off-Policy Evaluation via Human Inputs. AIES (2022)
Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. AISTATS (2022)
Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
OpenXAI: Towards a Transparent Evaluation of Model Explanations. CoRR (2022)
Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
Algorithmic Recourse in the Face of Noisy Human Responses. CoRR (2022)
Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
A Human-Centric Take on Model Monitoring. CoRR (2022)
Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
Data poisoning attacks on off-policy policy evaluation methods. UAI (2022)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations. CoRR (2022)
Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
A Human-Centric Perspective on Model Monitoring. HCOMP (2022)
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. CoRR (2022)
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. NeurIPS (2022)
Krishnaram Kenthapadi, Himabindu Lakkaraju, Pradeep Natarajan, Mehrnoosh Sameki
Model Monitoring in Practice: Lessons Learned and Open Challenges. KDD (2022)
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
Efficient Training of Low-Curvature Neural Networks. NeurIPS (2022)
Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. CoRR (2022)
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. CoRR (2022)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. CoRR (2022)
Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. AIES (2022)
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh
Rethinking Explainability as a Dialogue: A Practitioner's Perspective. CoRR (2022)
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
OpenXAI: Towards a Transparent Evaluation of Model Explanations. NeurIPS (2022)
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
Towards Robust Off-Policy Evaluation via Human Inputs. CoRR (2022)
Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju
Rethinking Stability for Attribution-based Explanations. CoRR (2022)
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. CoRR (2022)
Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS (2022)
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik
Evaluating Explainability for Graph Neural Networks. CoRR (2022)
Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju
On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR (2021)
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe
Fair Influence Maximization: a Welfare Optimization Approach. AAAI (2021)
Himabindu Lakkaraju
Towards Reliable and Practicable Algorithmic Recourse. CIKM (2021)
Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
Towards Robust and Reliable Algorithmic Recourse. NeurIPS (2021)
Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. NeurIPS (2021)
Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik
Towards a unified framework for fair and stable graph representation learning. UAI (2021)
Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring. AIES (2021)
Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik
Towards a Unified Framework for Fair and Stable Graph Representation Learning. CoRR (2021)
Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh
Counterfactual Explanations Can Be Manipulated. CoRR (2021)
Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh
Counterfactual Explanations Can Be Manipulated. NeurIPS (2021)
Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
Towards Robust and Reliable Algorithmic Recourse. CoRR (2021)
Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju
Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations. CoRR (2021)
Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
Feature Attributions and Counterfactual Explanations Can Be Manipulated. CoRR (2021)
Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
Learning Models for Actionable Recourse. NeurIPS (2021)
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. ICML (2021)
Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju
What will it take to generate fairness-preserving explanations? CoRR (2021)
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
Learning Under Adversarial and Interventional Shifts. CoRR (2021)
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR (2021)
Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju
Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses. CoRR (2020)
Kaivalya Rawal, Himabindu Lakkaraju
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. NeurIPS (2020)
Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez
Incorporating Interpretable Output Constraints in Bayesian Neural Networks. CoRR (2020)
Kaivalya Rawal, Himabindu Lakkaraju
Interpretable and Interactive Summaries of Actionable Recourses. CoRR (2020)
Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring. CoRR (2020)
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe
Fair Influence Maximization: A Welfare Optimization Approach. CoRR (2020)
Himabindu Lakkaraju, Nino Arsov, Osbert Bastani
Robust and Stable Black Box Explanations. ICML (2020)
Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. CoRR (2020)
Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. CoRR (2020)
Himabindu Lakkaraju, Osbert Bastani
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations. AIES (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Himabindu Lakkaraju