Robust Pattern Recognition Scheme for Devanagari Script. - Amit Dhurandhar; Kartik Shankarnarayanan; Rakesh Jawale (CIS (1), 2005)

Semi-analytical method for analyzing models and model selection measures based on moment analysis. - Amit Dhurandhar; Alin Dobra (ACM Trans. Knowl. Discov. Data, 2009)

Learning Maximum Lag for Grouped Graphical Granger Models. - Amit Dhurandhar (ICDM Workshops, 2010)

Multi-step Time Series Prediction in Complex Instrumented Domains. - Amit Dhurandhar (ICDM Workshops, 2010)

Managing Procurement Spend Using Advanced Compliance Analytics. - Pawan Chowdhary; Markus Ettl; Amit Dhurandhar; Soumyadip Ghosh; Gopikrishnan Maniachari; Bruce Graves; Bill Schaefer; Yu Tang (ICEBE, 2011)

Improving predictions using aggregate information. - Amit Dhurandhar (KDD, 2011)

Distribution-free bounds for relational classification. - Amit Dhurandhar; Alin Dobra (Knowl. Inf. Syst., 2012)

Intelligently querying incomplete instances for improving classification performance. - Karthik Sankaranarayanan; Amit Dhurandhar (CIKM, 2013)

Improving quality control by early prediction of manufacturing outcomes. - Sholom M. Weiss; Amit Dhurandhar; Robert J. Baseman (KDD, 2013)

Using coarse information for real valued prediction. - Amit Dhurandhar (Data Min. Knowl. Discov., 2013)

Single Network Relational Transductive Learning. - Amit Dhurandhar; Jun Wang 006 (J. Artif. Intell. Res., 2013)

Probabilistic characterization of nearest neighbor classifier. - Amit Dhurandhar; Alin Dobra (Int. J. Mach. Learn. Cybern., 2013)

Symmetric Submodular Clustering with Actionable Constraint. - Amit Dhurandhar; Karthik S. Gurumoorthy (CoRR, 2014)

Efficient and accurate methods for updating generalized linear models with multiple feature additions. - Amit Dhurandhar; Marek Petrik (J. Mach. Learn. Res., 2014)

Robust System for Identifying Procurement Fraud. - Amit Dhurandhar; Rajesh Kumar Ravi; Bruce Graves; Gopikrishnan Maniachari; Markus Ettl (AAAI, 2015)

Informative Prediction Based on Ordinal Questionnaire Data. - Tsuyoshi Idé; Amit Dhurandhar (ICDM, 2015)

Big Data System for Analyzing Risky Procurement Entities. - Amit Dhurandhar; Bruce Graves; Rajesh Kumar Ravi; Gopikrishnan Maniachari; Markus Ettl (KDD, 2015)

Bounds on the moments for an ensemble of random decision trees. - Amit Dhurandhar (Knowl. Inf. Syst., 2015)

Improving classification performance through selective instance completion. - Amit Dhurandhar; Karthik Sankaranarayanan (Mach. Learn., 2015)

Building an Interpretable Recommender via Loss-Preserving Transformation. - Amit Dhurandhar; Sechan Oh; Marek Petrik (CoRR, 2016)

Continuous prediction of manufacturing performance throughout the production lifecycle. - Sholom M. Weiss; Amit Dhurandhar; Robert J. Baseman; Brian F. White; Ronald Logan; Jonathan K. Winslow; Daniel J. Poindexter (J. Intell. Manuf., 2016)

Uncovering Group Level Insights with Accordant Clustering. - Amit Dhurandhar; Margareta Ackerman; Xiang Wang (SDM, 2017)

Learning with Changing Features. - Amit Dhurandhar; Steve Hanneke; Liu Yang 001 (CoRR, 2017)

TIP: Typifying the Interpretability of Procedures. - Amit Dhurandhar; Vijay S. Iyengar; Ronny Luss; Karthikeyan Shanmugam (CoRR, 2017)

A Formal Framework to Characterize Interpretability of Procedures. - Amit Dhurandhar; Vijay S. Iyengar; Ronny Luss; Karthikeyan Shanmugam (CoRR, 2017)

ProtoDash: Fast Interpretable Prototype Selection. - Karthik S. Gurumoorthy; Amit Dhurandhar; Guillermo A. Cecchi (CoRR, 2017)

Real-time understanding of humanitarian crises via targeted information retrieval. - Kien Pham; Prasanna Sattigeri; Amit Dhurandhar; A. C. Jacob; M. Vukovic; P. Chataigner; Juliana Freire; Aleksandra Mojsilovic; Kush R. Varshney (IBM J. Res. Dev., 2017)

Supervised item response models for informative prediction. - Tsuyoshi Idé; Amit Dhurandhar (Knowl. Inf. Syst., 2017)

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. - Amit Dhurandhar; Pin-Yu Chen; Ronny Luss; Chun-Chen Tu; Pai-Shun Ting; Karthikeyan Shanmugam; Payel Das (NeurIPS, 2018)

Improving Simple Models with Confidence Profiles. - Amit Dhurandhar; Karthikeyan Shanmugam; Ronny Luss; Peder A. Olsen (NeurIPS, 2018)

Teaching Meaningful Explanations. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2018)

Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection. - Karthik S. Gurumoorthy; Amit Dhurandhar (CoRR, 2018)

TED: Teaching AI to Explain its Decisions. - Michael Hind; Dennis Wei; Murray Campbell; Noel C. F. Codella; Amit Dhurandhar; Aleksandra Mojsilovic; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (AIES, 2019)

Efficient Data Representation by Selecting Prototypes with Importance Weights. - Karthik S. Gurumoorthy; Amit Dhurandhar; Guillermo A. Cecchi; Charu C. Aggarwal (ICDM, 2019)

Generating Contrastive Explanations with Monotonic Attribute Functions. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Karthikeyan Shanmugam; Chun-Chen Tu (CoRR, 2019)

Leveraging Simple Model Predictions for Enhancing its Performance. - Amit Dhurandhar; Karthikeyan Shanmugam; Ronny Luss (CoRR, 2019)

Model Agnostic Contrastive Explanations for Structured Data. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam; Ruchir Puri (CoRR, 2019)

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2019)

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. - 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; et al. (CoRR, 2019)

AI explainability 360: hands-on tutorial. - 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; et al. (FAT*, 2020)

Classifier Invariant Approach to Learn from Positive-Unlabeled Data. - Amit Dhurandhar; Karthik S. Gurumoorthy (ICDM, 2020)

Invariant Risk Minimization Games. - Kartik Ahuja; Karthikeyan Shanmugam; Kush R. Varshney; Amit Dhurandhar (ICML, 2020)

Enhancing Simple Models by Exploiting What They Already Know. - Amit Dhurandhar; Karthikeyan Shanmugam; Ronny Luss (ICML, 2020)

Tutorial on Human-Centered Explainability for Healthcare. - Prithwish Chakraborty; Bum Chul Kwon; Sanjoy Dey; Amit Dhurandhar; Daniel M. Gruen; Kenney Ng; Daby Sow; Kush R. Varshney (KDD, 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, 2020)

Learning Global Transparent Models from Local Contrastive Explanations. - Tejaswini Pedapati; Avinash Balakrishnan; Karthikeyan Shanmugam; Amit Dhurandhar (CoRR, 2020)

Learning to Initialize Gradient Descent Using Gradient Descent. - Kartik Ahuja; Amit Dhurandhar; Kush R. Varshney (CoRR, 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 Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

Anomaly Attribution with Likelihood Compensation. - Tsuyoshi Idé; Amit Dhurandhar; Jirí Navrátil 001; Moninder Singh; Naoki Abe (AAAI, 2021)

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. - Kartik Ahuja; Karthikeyan Shanmugam; Amit Dhurandhar (AISTATS, 2021)

AI Explainability 360 Toolkit. - 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; et al. (COMAD/CODS, 2021)

Treatment Effect Estimation Using Invariant Risk Minimization. - Abhin Shah; Kartik Ahuja; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney; Amit Dhurandhar (ICASSP, 2021)

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. - Kartik Ahuja; Jun Wang 006; Amit Dhurandhar; Karthikeyan Shanmugam; Kush R. Varshney (ICLR, 2021)

Leveraging Latent Features for Local Explanations. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Yunfeng Zhang; Karthikeyan Shanmugam; Chun-Chen Tu (KDD, 2021)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney (NeurIPS, 2021)

Towards Better Model Understanding with Path-Sufficient Explanations. - Ronny Luss; Amit Dhurandhar (CoRR, 2021)

Building Accurate Simple Models with Multihop. - Amit Dhurandhar; Tejaswini Pedapati (CoRR, 2021)

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; et al. (AAAI, 2022)

Let the CAT out of the bag: Contrastive Attributed explanations for Text. - Saneem A. Chemmengath; Amar Prakash Azad; Ronny Luss; Amit Dhurandhar (EMNLP, 2022)

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. - Q. Vera Liao; Yunfeng Zhang; Ronny Luss; Finale Doshi-Velez; Amit Dhurandhar (HCOMP, 2022)

Multihop: Leveraging Complex Models to Learn Accurate Simple Models. - Amit Dhurandhar; Tejaswini Pedapati (ICKG, 2022)

Auto-Transfer: Learning to Route Transferable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam; Pin-Yu Chen; Amit Dhurandhar (ICLR, 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 (NeurIPS, 2022)

Auto-Transfer: Learning to Route Transferrable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam; Pin-Yu Chen; Amit Dhurandhar (CoRR, 2022)

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners. - Karthikeyan Natesan Ramamurthy; Amit Dhurandhar; Dennis Wei; Zaid Bin Tariq (CoRR, 2022)

PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. - Shreyas Fadnavis; Amit Dhurandhar; Raquel Norel; Jenna M. Reinen; Carla Agurto; Erica Secchettin; Vittorio Schweiger; Giovanni Perini; Guillermo A. Cecchi (CoRR, 2022)

Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling. - Igor Melnyk; Vijil Chenthamarakshan; Pin-Yu Chen; Payel Das; Amit Dhurandhar; Inkit Padhi; Devleena Das (CoRR, 2022)

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. - Charvi Rastogi; Yunfeng Zhang; Dennis Wei; Kush R. Varshney; Amit Dhurandhar; Richard Tomsett (Proc. ACM Hum. Comput. Interact., 2022)

Human-centered explainability for life sciences, healthcare, and medical informatics. - 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 001 (Patterns, 2022)

Local Explanations for Reinforcement Learning. - Ronny Luss; Amit Dhurandhar; Miao Liu (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, 2023)

Explainable Cross-Topic Stance Detection for Search Results. - Tim Draws; Karthikeyan Natesan Ramamurthy; Ioana Baldini; Amit Dhurandhar; Inkit Padhi; Benjamin Timmermans; Nava Tintarev (CHIIR, 2023)

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. - Brianna Richardson; Prasanna Sattigeri; Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Amit Dhurandhar; Juan E. Gilbert (FAccT, 2023)

Reprogramming Pretrained Language Models for Antibody Sequence Infilling. - Igor Melnyk; Vijil Chenthamarakshan; Pin-Yu Chen; Payel Das; Amit Dhurandhar; Inkit Padhi; Devleena Das (ICML, 2023)

AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases. - Giridhar Ganapavarapu; Sumanta Mukherjee; Natalia Martinez Gil; Kanthi K. Sarpatwar; Amaresh Rajasekharan; Amit Dhurandhar; Vijay Arya; Roman Vaculín (KDD, 2023)

Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. - Jiajin Zhang; Hanqing Chao; Amit Dhurandhar; Pin-Yu Chen; Ali Tajer; Yangyang Xu; Pingkun Yan (MICCAI (1), 2023)

Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. - Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Kartik Ahuja; Vijay Arya (NeurIPS, 2023)

Atomist or holist? A diagnosis and vision for more productive interdisciplinary AI ethics dialogue. - Travis Greene; Amit Dhurandhar; Galit Shmueli (Patterns, 2023)

Ranking Large Language Models without Ground Truth. - Amit Dhurandhar; Rahul Nair; Moninder Singh; Elizabeth Daly; Karthikeyan Natesan Ramamurthy (ACL (Findings), 2024)

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. - Amit Dhurandhar; Tejaswini Pedapati; Ronny Luss; Soham Dan; Aurélie C. Lozano; Payel Das; Georgios Kollias (ACL (Findings), 2024)

Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization. - Naiyu Yin; Hanjing Wang; Yue Yu; Tian Gao 007; Amit Dhurandhar; Qiang Ji (ECCV (10), 2024)

Trust Regions for Explanations via Black-Box Probabilistic Certification. - Amit Dhurandhar; Swagatam Haldar; Dennis Wei; Karthikeyan Natesan Ramamurthy (ICML, 2024)

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. - Sahil Garg; Anderson Schneider; Anant Raj; Kashif Rasul; Yuriy Nevmyvaka; Sneihil Gopal; Amit Dhurandhar; Guillermo A. Cecchi; Irina Rish (CoRR, 2024)

CELL your Model: Contrastive Explanation Methods for Large Language Models. - Ronny Luss; Erik Miehling; Amit Dhurandhar (CoRR, 2024)

The global landscape of academic guidelines for generative AI and Large Language Models. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; David Atkinson; Amit Dhurandhar (CoRR, 2024)

Identifying Sub-networks in Neural Networks via Functionally Similar Representations. - Tian Gao 007; Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Dennis Wei (CoRR, 2024)

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 001 (CoRR, 2024)

When Stability meets Sufficiency: Informative Explanations that do not Overwhelm. - Ronny Luss; Amit Dhurandhar (Trans. Mach. Learn. Res., 2024)

To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. - Vijay Sadashivaiah; Keerthiram Murugesan; Ronny Luss; Pin-Yu Chen; Chris R. Sims; James A. Hendler; Amit Dhurandhar (Trans. Mach. Learn. Res., 2024)

Model Agnostic Contrastive Explanations for Classification Models. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam; Ruchir Puri (IEEE J. Emerg. Sel. Topics Circuits Syst., 2024)

Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents. - Ivoline C. Ngong; Swanand Ravindra Kadhe; Hao Wang 063; Keerthiram Murugesan; Justin D. Weisz; Amit Dhurandhar; Karthikeyan Natesan Ramamurthy (ACL (Findings), 2025)

Multi-Level Explanations for Generative Language Models. - 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 (ACL (1), 2025)

Programming Refusal with Conditional Activation Steering. - Bruce W. Lee; Inkit Padhi; Karthikeyan Natesan Ramamurthy; Erik Miehling; Pierre L. Dognin; Manish Nagireddy; Amit Dhurandhar (ICLR, 2025)

Large Language Model Confidence Estimation via Black-Box Access. - Tejaswini Pedapati; Amit Dhurandhar; Soumya Ghosh; Soham Dan; Prasanna Sattigeri (Trans. Mach. Learn. Res., 2025)

Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; David Atkinson; Amit Dhurandhar (CoRR, 2025)

IGGA: A Dataset of Industrial Guidelines and Policy Statements for Generative AIs. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; David Atkinson; Amit Dhurandhar (CoRR, 2025)

AGGA: A Dataset of Academic Guidelines for Generative AI and Large Language Models. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; David Atkinson; Amit Dhurandhar (CoRR, 2025)

LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; Abhejay Murali; David Atkinson; Amit Dhurandhar (CoRR, 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)

LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models. - Junfeng Jiao; Saleh Afroogh; Abhejay Murali; Kevin Chen; David Atkinson; Amit Dhurandhar (CoRR, 2025)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Kartikeyan Shanmugam; Dennis Wei; Kush R. Varshney (CoRR, 2025)

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions. - Junfeng Jiao; Saleh Afroogh; Kevin Chen; Abhejay Murali; David Atkinson; Amit Dhurandhar (CoRR, 2025)