Dhurandhar, Amit

33 publications

NeurIPS 2025 Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods Dennis Wei, Inkit Padhi, Soumya Ghosh, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Maria Chang
TMLR 2025 Large Language Model Confidence Estimation via Black-Box Access Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
ICLR 2025 Programming Refusal with Conditional Activation Steering Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre Dognin, Manish Nagireddy, Amit Dhurandhar
ECCV 2024 Integrating Markov Blanket Discovery into Causal Representation Learning for Domain Generalization Naiyu Yin, Hanjing Wang, Yue Yu, Tian Gao, Amit Dhurandhar, Qiang Ji
NeurIPSW 2024 Protecting Users from Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents Ivoline C. Ngong, Swanand Kadhe, Hao Wang, Keerthiram Murugesan, Justin D. Weisz, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy
TMLR 2024 To Transfer or Not to Transfer: Suppressing Concepts from Source Representations Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris Sims, James Hendler, Amit Dhurandhar
ICML 2024 Trust Regions for Explanations via Black-Box Probabilistic Certification Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
TMLR 2024 When Stability Meets Sufficiency: Informative Explanations That Do Not Overwhelm Ronny Luss, Amit Dhurandhar
NeurIPSW 2023 Causal Markov Blanket Representation Learning for Out-of-Distribution Generalization Naiyu Yin, Hanjing Wang, Tian Gao, Amit Dhurandhar, Qiang Ji
AAAI 2023 Local Explanations for Reinforcement Learning Ronny Luss, Amit Dhurandhar, Miao Liu
NeurIPS 2023 Locally Invariant Explanations: Towards Stable and Unidirectional Explanations Through Local Invariant Learning Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Kartik Ahuja, Vijay Arya
ICML 2023 Reprogramming Pretrained Language Models for Antibody Sequence Infilling Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
AAAI 2023 When Neural Networks Fail to Generalize? a Model Sensitivity Perspective Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
AAAI 2022 AI Explainability 360: Impact and Design Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
ICLR 2022 Auto-Transfer: Learning to Route Transferable Representations Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
NeurIPS 2022 Is This the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam
NeurIPS 2022 On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R Varshney, Elizabeth Daly, Moninder Singh
AISTATS 2021 Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
AAAI 2021 Anomaly Attribution with Likelihood Compensation Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
NeurIPS 2021 CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R Varshney
ICLR 2021 Empirical or Invariant Risk Minimization? a Sample Complexity Perspective Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
MLOSS 2020 AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
ICML 2020 Enhancing Simple Models by Exploiting What They Already Know Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
ICML 2020 Invariant Risk Minimization Games Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar
NeurIPS 2020 Learning Global Transparent Models Consistent with Local Contrastive Explanations Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
NeurIPS 2020 Model Agnostic Multilevel Explanations Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
NeurIPS 2018 Explanations Based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, Payel Das
NeurIPS 2018 Improving Simple Models with Confidence Profiles Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
MLJ 2015 Improving Classification Performance Through Selective Instance Completion Amit Dhurandhar, Karthik Sankaranarayanan
AAAI 2015 Robust System for Identifying Procurement Fraud Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl
JMLR 2014 Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions Amit Dhurandhar, Marek Petrik
JAIR 2013 Single Network Relational Transductive Learning Amit Dhurandhar, Jun Wang
JMLR 2008 Probabilistic Characterization of Random Decision Trees Amit Dhurandhar, Alin Dobra