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Treatment Effect Estimation using Invariant Risk Minimization


Mar 13, 2021
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush Varshney, Amit Dhurandhar


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Learning to Initialize Gradient Descent Using Gradient Descent


Dec 22, 2020
Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney


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Empirical or Invariant Risk Minimization? A Sample Complexity Perspective


Oct 30, 2020
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney


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Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions


Oct 28, 2020
Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar


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Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making


Oct 15, 2020
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett

* 15 pages, 5 figures 

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Invariant Risk Minimization Games


Mar 18, 2020
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar


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Model Agnostic Multilevel Explanations


Mar 12, 2020
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar

* 21 pages, 9 figures, 1 table 

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Learning Global Transparent Models from Local Contrastive Explanations


Feb 19, 2020
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar


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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques


Sep 14, 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 Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang


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Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning


Jun 05, 2019
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilović

* presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA. arXiv admin note: substantial text overlap with arXiv:1805.11648 

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Model Agnostic Contrastive Explanations for Structured Data


May 31, 2019
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri


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Leveraging Simple Model Predictions for Enhancing its Performance


May 30, 2019
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss


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Generating Contrastive Explanations with Monotonic Attribute Functions


May 29, 2019
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu


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TED: Teaching AI to Explain its Decisions


Nov 12, 2018
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

* This article leverages some content from arXiv:1805.11648 

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Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives


Oct 29, 2018
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, Payel Das


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TIP: Typifying the Interpretability of Procedures


Oct 29, 2018
Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam


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Teaching Meaningful Explanations


Sep 11, 2018
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

* 9 pages 

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Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection


Jul 21, 2018
Karthik S. Gurumoorthy, Amit Dhurandhar


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Improving Simple Models with Confidence Profiles


Jul 19, 2018
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder Olsen

* 16 pages 

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ProtoDash: Fast Interpretable Prototype Selection


Feb 03, 2018
Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi


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A Formal Framework to Characterize Interpretability of Procedures


Jul 12, 2017
Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam

* presented at 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), Sydney, NSW, Australia 

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Learning with Changing Features


Apr 29, 2017
Amit Dhurandhar, Steve Hanneke, Liu Yang


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Uncovering Group Level Insights with Accordant Clustering


Apr 07, 2017
Amit Dhurandhar, Margareta Ackerman, Xiang Wang

* accepted to SDM 2017 (oral) 

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Building an Interpretable Recommender via Loss-Preserving Transformation


Jun 19, 2016
Amit Dhurandhar, Sechan Oh, Marek Petrik

* Presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY 

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