Amit Dhurandhar

137 papers A* 30A 3B 2C 1Misc 1Journal 86Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Against AI ethics: challenging the conventional narratives. AI Ethics Saleh Afroogh, Yasser Pouresmaeil, Amit Dhurandhar
2026	J	`jnl`	AgentSCOPE: Evaluating Contextual Privacy Across Agentic Workflows. CoRR Ivoline C. Ngong, Keerthiram Murugesan, Swanand Kadhe, Justin D. Weisz, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy
2026	J	`jnl`	Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions. CoRR Saleh Afroogh, Seyd Ishtiaque Ahmed, Petra Ahrweiler, David Alvarez-Melis, Mansur Maturidi Arief, Emilia Barakova, Falco J. Bargagli-Stoffi, Erdem Biyik, Hanjie Chen, Xiang 'Anthony' Chen, Robert Clements, Keeley Crockett, Amit Dhurandhar, Fethiye Irmak Dogan, Mollie Dollinger, Motahhare Eslami, Aldo A. Faisal, Arya Farahi, Melanie Fernandez Pradie, Saadia Gabrie, Diego Garcia-Olano, Marzyeh Ghassemi, Shaona Ghosh, Hatice Gunes, Ehsan Hajiramezanali, Stefan Haufe, Biwei Huang, Angel Hwang, Md Tauhidul Islam, Junfeng Jiao, Amir-Hossein Karimi, Saber Kazeminasab, Anastasia Kuzminykh, William G. La Cava, Brian Y. Lim, Xiaofeng Liu, Mohammad R. K. Mofrad, Alicia Parrish, María Pérez-Ortiz, Shriti Raj, Swabha Swayamdipta, Salmon Talebi, Kush R. Varshney, Mihaela Vorvoreanu, Lily Weng, Alice Xiang, Yiming Xu, Ding Zhao, Jieyu Zhao
2026	J	`jnl`	CoFrGeNet: Continued Fraction Architectures for Language Generation. CoRR Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair
2025	J	`jnl`	AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar
2025	J	`jnl`	Agentic AI Needs a Systems Theory. CoRR Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Matthew Riemer, Djallel Bouneffouf, John T. Richards, Amit Dhurandhar, Elizabeth M. Daly, Michael Hind, Prasanna Sattigeri, Dennis Wei, Ambrish Rawat, Jasmina Gajcin, Werner Geyer
2025	J	`jnl`	CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. CoRR Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Kartikeyan Shanmugam, Dennis Wei, Kush R. Varshney
2025	J	`jnl`	Evaluating LLM Safety Across Child Development Stages: A Simulated Agent Approach. CoRR Abhejay Murali, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar, Junfeng Jiao
2025	J	`jnl`	Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar
2025	J	`jnl`	IGGA: A Dataset of Industrial Guidelines and Policy Statements for Generative AIs. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar
2025	J	`jnl`	LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models. CoRR Junfeng Jiao, Saleh Afroogh, Abhejay Murali, Kevin Chen, David Atkinson, Amit Dhurandhar
2025	J	`jnl`	LLM Harms: A Taxonomy and Discussion. CoRR Kevin Chen, Saleh Afroogh, Abhejay Murali, David Atkinson, Amit Dhurandhar, Junfeng Jiao
2025	J	`jnl`	LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar
2025	J	`jnl`	Large Language Model Confidence Estimation via Black-Box Access. Trans. Mach. Learn. Res. Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
2025	—	`conf`	Multi-Level Explanations for Generative Language Models. ACL (1) Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
2025	A*	`conf`	Programming Refusal with Conditional Activation Steering. ICLR Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre L. Dognin, Manish Nagireddy, Amit Dhurandhar
2025	—	`conf`	Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents. ACL (Findings) Ivoline C. Ngong, Swanand Ravindra Kadhe, Hao Wang, Keerthiram Murugesan, Justin D. Weisz, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy
2025	J	`jnl`	Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents. CoRR Ivoline C. Ngong, Swanand Kadhe, Hao Wang, Keerthiram Murugesan, Justin D. Weisz, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy
2025	J	`jnl`	Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar
2024	J	`jnl`	CELL your Model: Contrastive Explanation Methods for Large Language Models. CoRR Ronny Luss, Erik Miehling, Amit Dhurandhar
2024	J	`jnl`	Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. CoRR Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish
2024	J	`jnl`	Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods. CoRR Dennis Wei, Inkit Padhi, Soumya Ghosh, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Maria Chang
2024	J	`jnl`	Identifying Sub-networks in Neural Networks via Functionally Similar Representations. CoRR Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Dennis Wei
2024	—	`conf`	Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization. ECCV (10) Naiyu Yin, Hanjing Wang, Yue Yu, Tian Gao, Amit Dhurandhar, Qiang Ji
2024	J	`jnl`	Large Language Model Confidence Estimation via Black-Box Access. CoRR Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
2024	J	`jnl`	Model Agnostic Contrastive Explanations for Classification Models. IEEE J. Emerg. Sel. Topics Circuits Syst. Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
2024	J	`jnl`	Multi-Level Explanations for Generative Language Models. CoRR Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
2024	—	`conf`	NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
2024	J	`jnl`	NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
2024	J	`jnl`	Programming Refusal with Conditional Activation Steering. CoRR Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre L. Dognin, Manish Nagireddy, Amit Dhurandhar
2024	—	`conf`	Ranking Large Language Models without Ground Truth. ACL (Findings) Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
2024	J	`jnl`	Ranking Large Language Models without Ground Truth. CoRR Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
2024	J	`jnl`	The global landscape of academic guidelines for generative AI and Large Language Models. CoRR Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar
2024	J	`jnl`	To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. Trans. Mach. Learn. Res. Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar
2024	A*	`conf`	Trust Regions for Explanations via Black-Box Probabilistic Certification. ICML Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
2024	J	`jnl`	Trust Regions for Explanations via Black-Box Probabilistic Certification. CoRR Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
2024	J	`jnl`	When Stability meets Sufficiency: Informative Explanations that do not Overwhelm. Trans. Mach. Learn. Res. Ronny Luss, Amit Dhurandhar
2023	A*	`conf`	AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases. KDD Giridhar Ganapavarapu, Sumanta Mukherjee, Natalia Martinez Gil, Kanthi K. Sarpatwar, Amaresh Rajasekharan, Amit Dhurandhar, Vijay Arya, Roman Vaculín
2023	—	`conf`	Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Amit Dhurandhar, Juan E. Gilbert
2023	J	`jnl`	Atomist or holist? A diagnosis and vision for more productive interdisciplinary AI ethics dialogue. Patterns Travis Greene, Amit Dhurandhar, Galit Shmueli
2023	B	`conf`	Explainable Cross-Topic Stance Detection for Search Results. CHIIR Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans, Nava Tintarev
2023	A*	`conf`	Local Explanations for Reinforcement Learning. AAAI Ronny Luss, Amit Dhurandhar, Miao Liu
2023	A*	`conf`	Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. NeurIPS Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Kartik Ahuja, Vijay Arya
2023	A*	`conf`	Reprogramming Pretrained Language Models for Antibody Sequence Infilling. ICML Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
2023	—	`conf`	Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. MICCAI (1) Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
2023	J	`jnl`	Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. CoRR Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
2023	A*	`conf`	When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. AAAI Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
2022	A*	`conf`	AI Explainability 360: Impact and Design. AAAI 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
2022	J	`jnl`	Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners. CoRR Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq
2022	J	`jnl`	Anomaly Attribution with Likelihood Compensation. CoRR Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
2022	J	`jnl`	Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue. CoRR Travis Greene, Amit Dhurandhar, Galit Shmueli
2022	A*	`conf`	Auto-Transfer: Learning to Route Transferable Representations. ICLR Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
2022	J	`jnl`	Auto-Transfer: Learning to Route Transferrable Representations. CoRR Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
2022	B	`conf`	Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. HCOMP Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
2022	J	`jnl`	Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. CoRR Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
2022	J	`jnl`	Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum. Comput. Interact. Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
2022	J	`jnl`	Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns Sanjoy Dey, Prithwish Chakraborty, Bum Chul Kwon, Amit Dhurandhar, Mohamed F. Ghalwash, Fernando J. Suarez Saiz, Kenney Ng, Daby Sow, Kush R. Varshney, Pablo Meyer
2022	A*	`conf`	Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. NeurIPS Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam
2022	A*	`conf`	Let the CAT out of the bag: Contrastive Attributed explanations for Text. EMNLP Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
2022	J	`jnl`	Local Explanations for Reinforcement Learning. CoRR Ronny Luss, Amit Dhurandhar, Miao Liu
2022	J	`jnl`	Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. CoRR Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja, Vijay Arya
2022	—	`conf`	Multihop: Leveraging Complex Models to Learn Accurate Simple Models. ICKG Amit Dhurandhar, Tejaswini Pedapati
2022	A*	`conf`	On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. NeurIPS Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth Daly, Moninder Singh
2022	J	`jnl`	On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. CoRR Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh
2022	J	`jnl`	PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. CoRR Shreyas Fadnavis, Amit Dhurandhar, Raquel Norel, Jenna M. Reinen, Carla Agurto, Erica Secchettin, Vittorio Schweiger, Giovanni Perini, Guillermo A. Cecchi
2022	J	`jnl`	Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling. CoRR Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
2022	J	`jnl`	When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. CoRR Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
2021	—	`conf`	AI Explainability 360 Toolkit. COMAD/CODS 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
2021	J	`jnl`	AI Explainability 360: Impact and Design. CoRR 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
2021	A*	`conf`	Anomaly Attribution with Likelihood Compensation. AAAI Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
2021	J	`jnl`	Building Accurate Simple Models with Multihop. CoRR Amit Dhurandhar, Tejaswini Pedapati
2021	A*	`conf`	CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
2021	A*	`conf`	Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
2021	J	`jnl`	Let the CAT out of the bag: Contrastive Attributed explanations for Text. CoRR Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
2021	A*	`conf`	Leveraging Latent Features for Local Explanations. KDD Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
2021	A	`conf`	Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. AISTATS Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
2021	J	`jnl`	Towards Better Model Understanding with Path-Sufficient Explanations. CoRR Ronny Luss, Amit Dhurandhar
2021	Misc	`conf`	Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
2021	J	`jnl`	Treatment Effect Estimation using Invariant Risk Minimization. CoRR Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
2020	J	`jnl`	AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 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
2020	—	`conf`	AI explainability 360: hands-on tutorial. FAT* 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
2020	A*	`conf`	Classifier Invariant Approach to Learn from Positive-Unlabeled Data. ICDM Amit Dhurandhar, Karthik S. Gurumoorthy
2020	J	`jnl`	Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. CoRR Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
2020	J	`jnl`	Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
2020	A*	`conf`	Enhancing Simple Models by Exploiting What They Already Know. ICML Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
2020	A*	`conf`	Invariant Risk Minimization Games. ICML Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
2020	J	`jnl`	Invariant Risk Minimization Games. CoRR Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
2020	A*	`conf`	Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
2020	J	`jnl`	Learning Global Transparent Models from Local Contrastive Explanations. CoRR Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
2020	J	`jnl`	Learning to Initialize Gradient Descent Using Gradient Descent. CoRR Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney
2020	J	`jnl`	Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. CoRR Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
2020	A*	`conf`	Model Agnostic Multilevel Explanations. NeurIPS Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
2020	J	`jnl`	Model Agnostic Multilevel Explanations. CoRR Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
2020	A*	`conf`	Tutorial on Human-Centered Explainability for Healthcare. KDD Prithwish Chakraborty, Bum Chul Kwon, Sanjoy Dey, Amit Dhurandhar, Daniel M. Gruen, Kenney Ng, Daby Sow, Kush R. Varshney
2019	A*	`conf`	Efficient Data Representation by Selecting Prototypes with Importance Weights. ICDM Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi, Charu C. Aggarwal
2019	J	`jnl`	Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
2019	J	`jnl`	Leveraging Simple Model Predictions for Enhancing its Performance. CoRR Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
2019	J	`jnl`	Model Agnostic Contrastive Explanations for Structured Data. CoRR Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
2019	J	`jnl`	One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR 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
2019	C	`conf`	TED: Teaching AI to Explain its Decisions. AIES Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
2019	J	`jnl`	Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2018	A*	`conf`	Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. NeurIPS Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
2018	J	`jnl`	Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. CoRR Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
2018	A*	`conf`	Improving Simple Models with Confidence Profiles. NeurIPS Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
2018	J	`jnl`	Improving Simple Models with Confidence Profiles. CoRR Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
2018	J	`jnl`	Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection. CoRR Karthik S. Gurumoorthy, Amit Dhurandhar
2018	J	`jnl`	TED: Teaching AI to Explain its Decisions. CoRR Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2018	J	`jnl`	Teaching Meaningful Explanations. CoRR Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2017	J	`jnl`	A Formal Framework to Characterize Interpretability of Procedures. CoRR Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
2017	J	`jnl`	Learning with Changing Features. CoRR Amit Dhurandhar, Steve Hanneke, Liu Yang
2017	J	`jnl`	ProtoDash: Fast Interpretable Prototype Selection. CoRR Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi
2017	J	`jnl`	Real-time understanding of humanitarian crises via targeted information retrieval. IBM J. Res. Dev. Kien Pham, Prasanna Sattigeri, Amit Dhurandhar, Arpith C. Jacob, Maja Vukovic, Patrice Chataigner, Juliana Freire, Aleksandra Mojsilovic, Kush R. Varshney
2017	J	`jnl`	Supervised item response models for informative prediction. Knowl. Inf. Syst. Tsuyoshi Idé, Amit Dhurandhar
2017	J	`jnl`	TIP: Typifying the Interpretability of Procedures. CoRR Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
2017	A	`conf`	Uncovering Group Level Insights with Accordant Clustering. SDM Amit Dhurandhar, Margareta Ackerman, Xiang Wang
2017	J	`jnl`	Uncovering Group Level Insights with Accordant Clustering. CoRR Amit Dhurandhar, Margareta Ackerman, Xiang Wang
2016	J	`jnl`	Building an Interpretable Recommender via Loss-Preserving Transformation. CoRR Amit Dhurandhar, Sechan Oh, Marek Petrik
2016	J	`jnl`	Continuous prediction of manufacturing performance throughout the production lifecycle. J. Intell. Manuf. Sholom M. Weiss, Amit Dhurandhar, Robert J. Baseman, Brian F. White, Ronald Logan, Jonathan K. Winslow, Daniel J. Poindexter
2015	A*	`conf`	Big Data System for Analyzing Risky Procurement Entities. KDD Amit Dhurandhar, Bruce Graves, Rajesh Kumar Ravi, Gopikrishnan Maniachari, Markus Ettl
2015	J	`jnl`	Bounds on the moments for an ensemble of random decision trees. Knowl. Inf. Syst. Amit Dhurandhar
2015	J	`jnl`	Improving classification performance through selective instance completion. Mach. Learn. Amit Dhurandhar, Karthik Sankaranarayanan
2015	A*	`conf`	Informative Prediction Based on Ordinal Questionnaire Data. ICDM Tsuyoshi Idé, Amit Dhurandhar
2015	A*	`conf`	Robust System for Identifying Procurement Fraud. AAAI Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl
2014	J	`jnl`	Efficient and accurate methods for updating generalized linear models with multiple feature additions. J. Mach. Learn. Res. Amit Dhurandhar, Marek Petrik
2014	J	`jnl`	Symmetric Submodular Clustering with Actionable Constraint. CoRR Amit Dhurandhar, Karthik S. Gurumoorthy
2013	A*	`conf`	Improving quality control by early prediction of manufacturing outcomes. KDD Sholom M. Weiss, Amit Dhurandhar, Robert J. Baseman
2013	A	`conf`	Intelligently querying incomplete instances for improving classification performance. CIKM Karthik Sankaranarayanan, Amit Dhurandhar
2013	J	`jnl`	Probabilistic characterization of nearest neighbor classifier. Int. J. Mach. Learn. Cybern. Amit Dhurandhar, Alin Dobra
2013	J	`jnl`	Single Network Relational Transductive Learning. J. Artif. Intell. Res. Amit Dhurandhar, Jun Wang
2013	J	`jnl`	Using coarse information for real valued prediction. Data Min. Knowl. Discov. Amit Dhurandhar
2012	J	`jnl`	Distribution-free bounds for relational classification. Knowl. Inf. Syst. Amit Dhurandhar, Alin Dobra
2011	A*	`conf`	Improving predictions using aggregate information. KDD Amit Dhurandhar
2011	—	`conf`	Managing Procurement Spend Using Advanced Compliance Analytics. ICEBE Pawan Chowdhary, Markus Ettl, Amit Dhurandhar, Soumyadip Ghosh, Gopikrishnan Maniachari, Bruce Graves, Bill Schaefer, Yu Tang
2010	—	`conf`	Learning Maximum Lag for Grouped Graphical Granger Models. ICDM Workshops Amit Dhurandhar
2010	—	`conf`	Multi-step Time Series Prediction in Complex Instrumented Domains. ICDM Workshops Amit Dhurandhar
2009	J	`jnl`	Semi-analytical method for analyzing models and model selection measures based on moment analysis. ACM Trans. Knowl. Discov. Data Amit Dhurandhar, Alin Dobra
2005	—	`conf`	Robust Pattern Recognition Scheme for Devanagari Script. CIS (1) Amit Dhurandhar, Kartik Shankarnarayanan, Rakesh Jawale