Amit Dhurandhar

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 92

Top Venues

Publications

Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson, Amit Dhurandhar
The global landscape of academic guidelines for generative AI and Large Language Models. CoRR (2024)
Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
Large Language Model Confidence Estimation via Black-Box Access. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR (2024)
Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
Ranking Large Language Models without Ground Truth. ACL (Findings) (2024)
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
Multi-Level Explanations for Generative Language Models. CoRR (2024)
Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
Ranking Large Language Models without Ground Truth. CoRR (2024)
Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. CoRR (2024)
Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
Trust Regions for Explanations via Black-Box Probabilistic Certification. CoRR (2024)
Ronny Luss, Erik Miehling, Amit Dhurandhar
CELL your Model: Contrastive Explanation Methods for Large Language Models. CoRR (2024)
Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. Trans. Mach. Learn. Res. 2024 (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) (2024)
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. CoRR (2023)
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Reprogramming Pretrained Language Models for Antibody Sequence Infilling. ICML (2023)
Giridhar Ganapavarapu, Sumanta Mukherjee, Natalia Martinez Gil, Kanthi K. Sarpatwar, Amaresh Rajasekharan, Amit Dhurandhar, Vijay Arya, Roman Vaculín
AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases. KDD (2023)
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. MICCAI (1) (2023)
Travis Greene, Amit Dhurandhar, Galit Shmueli
Atomist or holist? A diagnosis and vision for more productive interdisciplinary AI ethics dialogue. Patterns 4 (1) (2023)
Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans, Nava Tintarev
Explainable Cross-Topic Stance Detection for Search Results. CHIIR (2023)
Ronny Luss, Amit Dhurandhar, Miao Liu
Local Explanations for Reinforcement Learning. AAAI (2023)
Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Kartik Ahuja, Vijay Arya
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. NeurIPS (2023)
Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush Varshney, Amit Dhurandhar, Juan E. Gilbert
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT (2023)
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. AAAI (2023)
Ronny Luss, Amit Dhurandhar, Miao Liu
Local Explanations for Reinforcement Learning. CoRR (2022)
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. CoRR (2022)
Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
Let the CAT out of the bag: Contrastive Attributed explanations for Text. EMNLP (2022)
Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. CoRR (2022)
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
Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns 3 (5) (2022)
Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. CoRR (2022)
Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners. CoRR (2022)
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling. CoRR (2022)
Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth Daly, Moninder Singh
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. NeurIPS (2022)
Travis Greene, Amit Dhurandhar, Galit Shmueli
Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue. CoRR (2022)
Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
Anomaly Attribution with Likelihood Compensation. CoRR (2022)
Shreyas Fadnavis, Amit Dhurandhar, Raquel Norel, Jenna M. Reinen, Carla Agurto, Erica Secchettin, Vittorio Schweiger, Giovanni Perini, Guillermo A. Cecchi
PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. CoRR (2022)
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
AI Explainability 360: Impact and Design. AAAI (2022)
Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. HCOMP (2022)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferable Representations. ICLR (2022)
Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. NeurIPS (2022)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferrable Representations. CoRR (2022)
Amit Dhurandhar, Tejaswini Pedapati
Multihop: Leveraging Complex Models to Learn Accurate Simple Models. ICKG (2022)
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum. Comput. Interact. 6 (CSCW1) (2022)
Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja, Vijay Arya
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. CoRR (2022)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR (2021)
Amit Dhurandhar, Tejaswini Pedapati
Building Accurate Simple Models with Multihop. CoRR (2021)
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. AISTATS (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation using Invariant Risk Minimization. CoRR (2021)
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
AI Explainability 360 Toolkit. COMAD/CODS (2021)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
Leveraging Latent Features for Local Explanations. KDD (2021)
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
AI Explainability 360: Impact and Design. CoRR (2021)
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP (2021)
Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe
Anomaly Attribution with Likelihood Compensation. AAAI (2021)
Saneem Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
Let the CAT out of the bag: Contrastive Attributed explanations for Text. CoRR (2021)
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS (2021)
Ronny Luss, Amit Dhurandhar
Towards Better Model Understanding with Path-Sufficient Explanations. CoRR (2021)
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. CoRR (2020)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models from Local Contrastive Explanations. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. ICML (2020)
Amit Dhurandhar, Karthik S. Gurumoorthy
Classifier Invariant Approach to Learn from Positive-Unlabeled Data. ICDM (2020)
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
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
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
AI explainability 360: hands-on tutorial. FAT* (2020)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Enhancing Simple Models by Exploiting What They Already Know. ICML (2020)
Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney
Learning to Initialize Gradient Descent Using Gradient Descent. CoRR (2020)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS (2020)
Prithwish Chakraborty, Bum Chul Kwon, Sanjoy Dey, Amit Dhurandhar, Daniel Gruen, Kenney Ng, Daby Sow, Kush R. Varshney
Tutorial on Human-Centered Explainability for Healthcare. KDD (2020)
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
Model Agnostic Multilevel Explanations. NeurIPS (2020)
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
Invariant Risk Minimization Games. CoRR (2020)
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. CoRR (2020)
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
Model Agnostic Multilevel Explanations. CoRR (2020)
Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
TED: Teaching AI to Explain its Decisions. AIES (2019)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR (2019)
Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi, Charu C. Aggarwal
Efficient Data Representation by Selecting Prototypes with Importance Weights. ICDM (2019)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Leveraging Simple Model Predictions for Enhancing its Performance. CoRR (2019)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR (2019)
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
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR (2019)
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
Model Agnostic Contrastive Explanations for Structured Data. CoRR (2019)
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. NeurIPS (2018)
Karthik S. Gurumoorthy, Amit Dhurandhar
Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection. CoRR (2018)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
TED: Teaching AI to Explain its Decisions. CoRR (2018)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
Improving Simple Models with Confidence Profiles. CoRR (2018)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
Improving Simple Models with Confidence Profiles. NeurIPS (2018)
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
Teaching Meaningful Explanations. CoRR (2018)
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. CoRR (2018)
Kien Pham, Prasanna Sattigeri, Amit Dhurandhar, A. C. Jacob, M. Vukovic, P. Chataigner, Juliana Freire, Aleksandra Mojsilovic, Kush R. Varshney
Real-time understanding of humanitarian crises via targeted information retrieval. IBM J. Res. Dev. 61 (6) (2017)
Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
TIP: Typifying the Interpretability of Procedures. CoRR (2017)
Amit Dhurandhar, Margareta Ackerman, Xiang Wang
Uncovering Group Level Insights with Accordant Clustering. SDM (2017)
Amit Dhurandhar, Steve Hanneke, Liu Yang
Learning with Changing Features. CoRR (2017)
Tsuyoshi Idé, Amit Dhurandhar
Supervised item response models for informative prediction. Knowl. Inf. Syst. 51 (1) (2017)
Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi
ProtoDash: Fast Interpretable Prototype Selection. CoRR (2017)
Amit Dhurandhar, Margareta Ackerman, Xiang Wang
Uncovering Group Level Insights with Accordant Clustering. CoRR (2017)
Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
A Formal Framework to Characterize Interpretability of Procedures. CoRR (2017)
Sholom M. Weiss, Amit Dhurandhar, Robert J. Baseman, Brian F. White, Ronald Logan, Jonathan K. Winslow, Daniel J. Poindexter
Continuous prediction of manufacturing performance throughout the production lifecycle. J. Intell. Manuf. 27 (4) (2016)
Amit Dhurandhar, Sechan Oh, Marek Petrik
Building an Interpretable Recommender via Loss-Preserving Transformation. CoRR (2016)
Tsuyoshi Idé, Amit Dhurandhar
Informative Prediction Based on Ordinal Questionnaire Data. ICDM (2015)
Amit Dhurandhar, Karthik Sankaranarayanan
Improving classification performance through selective instance completion. Mach. Learn. 100 (2-3) (2015)
Amit Dhurandhar, Bruce Graves, Rajesh Kumar Ravi, Gopikrishnan Maniachari, Markus Ettl
Big Data System for Analyzing Risky Procurement Entities. KDD (2015)
Amit Dhurandhar
Bounds on the moments for an ensemble of random decision trees. Knowl. Inf. Syst. 44 (2) (2015)
Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl
Robust System for Identifying Procurement Fraud. AAAI (2015)
Amit Dhurandhar, Karthik S. Gurumoorthy
Symmetric Submodular Clustering with Actionable Constraint. CoRR (2014)
Amit Dhurandhar, Marek Petrik
Efficient and accurate methods for updating generalized linear models with multiple feature additions. J. Mach. Learn. Res. 15 (1) (2014)
Amit Dhurandhar
Using coarse information for real valued prediction. Data Min. Knowl. Discov. 27 (2) (2013)

Amit Dhurandhar.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Amit Dhurandhar