Amit Dhurandhar           

Publications

2023

• Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee Pereira, James A. Hendler, Jonathan S. Dordick and Payel Das. Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations. Nature Scientific Reports, 2023. article

• Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini Soares, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans and Nava Tintarev. Explainable Cross-Topic Stance Detection for Search Results. ACM SIGIR Conference On Human Information Interaction And Retrieval (CHIIR), 2023. article

• Ronny Luss*, Amit Dhurandhar* and Miao Liu. Local Explanations for Reinforcement Learning. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. article

• Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu and Pingkun Yan. When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. article

• Travis Greene, Amit Dhurandhar and Galit Shmueli. Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue. Cell Patterns, 2023 (invited). article

2022

• Shreyas Fadnavis, Amit Dhurandhar, Raquel Norel, Jenna M Reinen, Carla Agurto, Erica Secchettin, Vittorio Schweiger, Giovanni Perini and Guillermo Cecchi. PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. Machine Learning for Health (ML4H), 2022 (extended abstract). article

• Saneem Chemmengath, Amar Prakash Azad, Ronny Luss and Amit Dhurandhar. Let the CAT out of the bag: Contrastive Attributed explanations for Text. Empirical Methods in Natural Language Processing (EMNLP), 2022. article

• Amit Dhurandhar, Karthikeyan Natesan Ramamurthy and Karthikeyan Shanmugam. Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. Advances in Neural Information Processing Systems (NeurIPS), 2022. article

• Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly and Moninder Singh. On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. Advances in Neural Information Processing Systems (NeurIPS), 2022. article

• Amit Dhurandhar* and Tejaswini Pedapati*. Multihop: Leveraging Complex Models to Learn Accurate Simple Models. Proceedings of the IEEE Intl. Conference on Knowledge Graphs (ICKG), 2022. article

• Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez and Amit Dhurandhar. Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. Proceedings of the Tenth Intl. AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022 (Best paper honorable mention). article

• Sanjoy Dey, Prithwish Chakraborty, Bum Chul Kwon, Amit Dhurandhar, Mohamed Ghalwash, Fernando J. Suarez Saiz, Kenney Ng, Daby Sow, Kush R. Varshney and Pablo Meyer. Human-Centered Explainability for Life Sciences, Healthcare and Medical Informatics. Cell Patterns, 2022. article

• Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen and Amit Dhurandhar. Auto-Transfer: Learning to Route Transferable Representations. Proceedings of the Tenth Intl. Conference on Learning Representations (ICLR), 2022. article

• Charvi Ratogi, Yunfeng Zhang, Dennis Wei, Kush Varshney, Amit Dhurandhar and Richard Tomsett. Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. ACM Conference On Computer- Supported Cooperative Work And Social Computing (CSCW), 2022. article

• 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. AI Explainability 360: Impact and Design. Innovative Application of Artificial Intelligence (IAAI), 2022. article

2021

• Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee Pereira, James Hendler, Jonathan S. Dordick and Payel Das. Accurate Multi-Endpoint Molecular Toxicity Predictions in Humans with Contrastive Explanations. NeurIPS Workshop on Women in Machine Learning (WIML-NeurIPS), 2021.

• Devleena Das, Inkit Padhi, Payel Das, Pin-Yu Chen and Amit Dhurandhar. Leveraging Adversarial Reprogramming for Template-Constrained Protein Sequence Design. NeurIPS Workshop on Learning Meaningful Representations of Life (LMLR-NeurIPS), 2021.

• Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei and Kush Varshney. CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. Advances in Neural Information Processing Systems (NeurIPS), 2021. article

• Ronny Luss*, Pin-Yu Chen*, Amit Dhurandhar*, Prasanna Sattigeri*, Yunfeng Zhang*, Karthikeyan Shanmugam and Chun-Chen Tu. Leveraging Latent Features for Local Explanations. ACM conference on Knowledge Discovery and Data Mining (KDD), 2021 (oral). arxiv

• Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush Varshney and Amit Dhurandhar. Invariant Risk Minimization based Treatement Effect Estimation. Proceedings of the IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

• Kartik Ahuja, Karthikeyan Shanmugam and Amit Dhurandhar. Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. Proceedings of the Intl. Conference on Artificial Intelligence and Statistics (AISTATS), 2021. arxiv

• Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam and Kush Varshney. Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. Proceedings of the Nineth Intl. Conference on Learning Representations (ICLR), 2021. arxiv

• Tsuyoshi Ide, Amit Dhurandhar, Jiri Navratil, Moninder Singh and Naoki Abe. Anomaly Attribution with Likelihood Compensation. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021.

2020

• Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam and Amit Dhurandhar. Learning Global Transparent Models from Local Contrastive Explanations. Advances in Neural Information Processing Systems (NeurIPS), 2020. arxiv

• Karthikeyan Ramamurthy, Bhanu Vinzamuri, Yunfeng Zhang and Amit Dhurandhar. Model Agnostic Multilevel Explanations. Advances in Neural Information Processing Systems (NeurIPS), 2020. arxiv

• Amit Dhurandhar and Karthik Gurumoorthy. Classifier Invariant Approach to Learn from Positive-Unlabeled Data. IEEE Conference on Data Mining (ICDM), 2020 (Best of ICDM). PDF

• Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, and Richard Tomsett. Human Cognitive Biases in Interpreting Machine Learning. INFORMS Annual Meeting, 2020.

• Amit Dhurandhar, Karthikeyan Shanmugam and Ronny Luss. Enhancing Simple Models by Exploiting What they Already Know. Intl. Conference on Machine Learning (ICML), 2020. arxiv

• Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney and Amit Dhurandhar. Invariant Risk Minimization Games. Intl. Conference on Machine Learning (ICML), 2020. arxiv

• Amit Dhurandhar, Karthikeyan Shanmugam, and Ronny Luss. Leveraging Simple Model Predictions for Enhancing its Performance. ICML Workshop on XXAI: Extending Explainable AI Beyond Deep Models and Classifiers (XXAI-ICML), 2020.

• Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, and Amit Dhurandhar. On the Equivalence of Bi-Level Optimization and Game-Theoretic Formulations of Invariant Risk Minimization. ICML Workshop on Inductive Biases, Invariances, and Generalization in RL (IIGRL-ICML), 2020.

• Bum Chul Kwon, Prithwish Chakraborty, James Codella, Amit Dhurandhar, Daby Sow and Kenney Ng. Visually Exploring Contrastive Explanation for Diagnostic Risk Prediction on Electronic Health Records. ICML Workshop on Human Interpretability in Machine Learning (WHI-ICML), 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 Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang. AI Explainability 360. An Extensible Toolkit of AI Explainability Algorithms. Journal of Machine Learning Research (JMLR), 2020. article

2019

• Karthik Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi and Charu Aggarwal. Efficient Data Representation by Selecting Prototypes with Importance Weights. IEEE Conference on Data Mining (ICDM), 2019. arxiv

• Noel Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei and Aleksandra Mojsilovic. Teaching Meaningful Explanations. ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2019 (oral). PDF

2018

• Amit Dhurandhar, Vijay Iyengar, Ronny Luss and Karthikeyan Shanmugam. TIP: Typifying the Interpretability of Procedures. arxiv

• Etkin Gutierrez, Amit Dhurandhar, Andreas Keller, Guillermo Cecchi, Pablo Meyer. Predicting natural language descriptions of mono-molecular odorants. Nature Communications, 2018. article

• Amit Dhurandhar*, Pin-Yu Chen*, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam and Payel Das. Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. Advances in Neural Information Processing Systems (NeurIPS), 2018. PDF, supplementary material, 3-minute video (Featured in Forbes and PC magazine)

• Amit Dhurandhar*, Karthikeyan Shanmugam*, Ronny Luss and Peder Olsen. Improving Simple Models using Confidence Profiles. Advances in Neural Information Processing Systems (NeurIPS), 2018. PDF, supplementary material, 3-minute video (Featured in PC magazine and Medium)

2017

• Andreas Keller*, Richard C. Gerkin*, Yuanfang Guan*, Amit Dhurandhar, Gabor Turu, Bence Szalai, Joel D.Mainland, Yusuke Ihara,Chung Wen Yu, Russ Wolfinger, Celine Vens, Leander Schietgat, Kurt De Grave, Raquel Norel, DREAM Olfaction Consortium, Gustavo Stolovitzky, Guillermo Cecchi, Leslie B. Vosshall, and Pablo Meyer. Predicting Human Olfactory Perception from Chemical Features of Odor Molecules. Science, 2017. article (Highlighted at AAAS meeting as a breakthrough in the last 3 decades in olfactory research)

• Real-Time Understanding of Humanitarian Crises via Targeted Information Retrieval. Kien T. Pham, Prasanna Sattigeri, Amit Dhurandhar, Arpith C. Jacob, Maja Vukovic, Patrice Chataigner, Juliana Freire, Aleksandra Mojsilović, and Kush R. Varshney. IBM Journal of Research and Development, 2017.

• Amit Dhurandhar, Steve Hanneke and Liu Yang. Learning with Changing features. arxiv

• Amit Dhurandhar, Vijay Iyengar, Ronny Luss and Karthikeyan Shanmugam. A Formal Framework to Characterize Interpretability of Procedures. Human Interpretability in Machine Learning workshop in Intl. Conference on Machine Learning (WHI-ICML), 2017. PDF

• Amit Dhurandhar, Margareta Ackerman and Xiang Wang. Uncovering Group Level Insights with Accordant Clustering. SIAM Intl. Conference on Data Mining (SDM), 2017. PDF, supplementary material

2016

• Tsuyoshi Ide and Amit Dhurandhar. Supervised Item Response Models for Informative Prediction. Knowledge and Information Systems (KAIS), 2016. The final publication is available at official link and the personal version is here PDF (invited)

• Amit Dhurandhar, Sechan Oh and Marek Petrik. Building Interpretable Recommender via Loss-Preserving Transformation. Human Interpretability in Machine Learning workshop in Intl. Conference on Machine Learning (WHI-ICML), 2016. PDF

2015

• Amit Dhurandhar and Karthik Sankarnarayanan. Improving Classification Performance through Selective Instance Completion. Machine Learning Journal (MLJ), 2015. The final publication is available at official link and the personal version is here PDF (with presentation slot at ECML 2015)

• Amit Dhurandhar, Bruce Graves, Rajesh Ravi, Gopikrishnan Maniachari and Markus Ettl. Big Data System for Analyzing Risky Entities. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2015 (oral). PDF

• Tsuyoshi Ide and Amit Dhurandhar. Informative Prediction based on Ordinal Questionnaire Data. IEEE Intl. Conference on Data Mining (ICDM), 2015 (Best of ICDM) PDF

• Amit Dhurandhar, Rajesh Ravi, Bruce Graves, Gopikrishnan Maniachari and Markus Ettl. Robust System for Identifying Procurement Fraud. Innovative Applications of Artificial Intelligence (IAAI), 2015. (Deployed Application Award)

2014

• Amit Dhurandhar and Marek Petrik. Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions. Journal of Machine Learning Research (JMLR), 2014. PDF

• Amit Dhurandhar. Bounds on the Moments for an Ensemble of Random Decision Trees. Knowledge and Information Systems (KAIS), 2014.The final publication is available at official link and the personal version is here PDF

• Sholom Weiss, Amit Dhurandhar, Robert Baseman, Brian White, Ronald Logan, Jonathan Winslow and Daniel Poindexter. Continuous Prediction of Manufacturing Outcomes Throughout the Production Lifecycle. Journal of Intelligent Manufacturing (JIMS), 2014. The final publication is available at official link and the personal version is here PDF

• Amit Dhurandhar and Karthik Gurumoorthy. Symmetric Submodular Clustering with Actionable Constraint. Discrete Optimization workshop in, (WDO-NIPS) 2014. PDF

• Rajesh Ravi, Amit Dhurandhar, Markus Ettl, Bruce Graves. Procurement Fraud Risk Analytics Tool. Information on Demand Conference (IOD), 2014.

2013

• Amit Dhurandhar and Jun Wang. Single Network Relational Transductive Learning. Journal of Artificial Intelligence Research (JAIR), 2013. PDF

• Amit Dhurandhar. Using Coarse Information for Real Valued Prediction. Data Mining and Knowledge Discovery (DMKD), 2013. (nominated for IBM Pat Goldberg Award) The final publication is available at official link and the personal version is here PDF

• Sholom Weiss, Amit Dhurandhar and Robert Baseman. Improving Quality Control by Early Prediction of Manufacturing Outcomes. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2013 (oral). PDF

• Karthik Sankarnarayanan and Amit Dhurandhar. Intelligently Querying Incomplete Instances for Improving Classification Performance. ACM International Conference on Information and Knowledge Management (CIKM), 2013 (full paper, oral). PDF

• Amit Dhurandhar. Auto-correlation Dependent Bounds for Relational Data. Mining and Learning over Graphs workshop in ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2013. PDF

2012

• Amit Dhurandhar and Alin Dobra. Probabilistic Characterization of Nearest Neighbor Classifiers. Intl. Journal of Machine Learning and Cybernetics (IJMLC), 2012. (invited) The final publication is available at official link and the personal version is here PDF

• Amit Dhurandhar and Alin Dobra. Distribution free bounds for Relational Classification. Knowledge and Information Systems (KAIS), 2012. The final publication is available at official link and the personal version is here PDF

2011

• Amit Dhurandhar. Improving Predictions using Aggregate Information. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2011. PDF

• Pawan Chowdhary, Markus Ettl, Amit Dhurandhar, Soumyadip Ghosh, Gopikrishna Maniachari, Bruce Graves, Bill Schaefer and Yu Tang. Identify and Manage Procurement Savings using Advanced Compliance Analytics. IEEE International Conference on e-Business Engineering (ICEBE), 2011. PDF

2010 (and before)

• Amit Dhurandhar. Multistep Time Series Prediction in Complex Instrumented Domains. Large scale analytics in complex instrumented domains workshop in IEEE International Conference on Data Mining (ICDMW), 2010. PDF This paper was also invited to Chance Discovery workshop in (IJCAI), 2011.

• Amit Dhurandhar. Learning Maximum Lag for Grouped Graphical Granger Models. Knowledge Discovery from Climate Data Prediction, Extremes, and Impacts workshop in IEEE International Conference on Data Mining (ICDMW), 2010. PDF

• Dan Connors, Amit Dhurandhar, Markus Ettl, Mary Helander, Jayant Kalagnanam, Shubir Kapoor, Ramesh Natarajan, Stuart Seigal, Zhackary Xue. Demand forecasting and supply chain optimization using freshness. Information on Demand Conference (IOD), 2010.

• Robert Baseman, Amit Dhurandhar, Michal Ozery and Naama Perush. Statistical Assessment of dissimilarities in trace data of unusual and nominal wafers. ISMI manufacturing week, 2010.

• Robert Baseman, Frances Clougherty, Amit Dhurandhar, Lyndon Logan, Daniel Poindexter, Brian White, Sholom Weiss, Jonathan Winslow, Denis Zhereschin. Early Predictions of Device Performance for Enhanced Process Control and Operations Optimization. ISMI Symposium on manufacturing excellence, 2010.

• John Andrews, Robert Baseman, Michael Biagetti, Amit Dhurandhar, Hong Lin, Michal Ozery-Flato, Stuart A Siegel, Naama Parush-Shear-Yashuv, Adam Ticknor. Utilization of Equipment Trace Data in a 300mm Semiconductor Fab. ISMI Symposium on manufacturing excellence, 2010.

• Amit Dhurandhar and Alin Dobra. Semi-analytical Method for Analyzing Models and Model Selection Measures based on Moment Analysis. ACM Transactions on Knowledge Discovery from Data (TKDD), 2009. PDF

• Amit Dhurandhar and Alin Dobra. Evaluating Evaluation Measures. Evaluation Methods in Machine Learning workshop in International Conference on Machine Learning (WEMML-ICML), 2009. PDF

• Amit Dhurandhar and Alin Dobra. Probabilistic Characterization of Random Decision Trees. Journal of Machine Learning Research (JMLR), 2008. PDF

• Amit Dhurandhar and Alin Dobra. Study of Classification Algorithms using Moment Analysis. One of 2 regular papers accepted to New Challenges in Theoretical Machine Learning workshop in Neural Information Processing Systems (WTML-NIPS), 2008. PDF Talk link

• Amit Dhurandhar, Kartik Shankar and Rakesh Jawale. Robust Pattern Recognition Scheme for Devanagari Script. IEEE International Conference on Computational Intelligence and Security (CIS) 2005.

Technical Reports

• Amit Dhurandhar and Alin Dobra. Test Set Bounds for Relational Data that vary with Strength of Dependence. PDF
• Amit Dhurandhar and Paul Gader. Output Distribution of Choquet Integral. PDF
• Amit Dhurandhar and Alin Dobra. Insights into Cross-validation. PDF
• Amit Dhurandhar and Alin Dobra. Independent vs Collective Classification in Statistical Relational Learning. PDF

Patents

• Amit Dhurandhar, Karthikeyan Ramamurthy and Karthikeyan Shanmugam. Accurate and Query-Efficient Model Agnostic Explanations. P202203278US01

• Amit Dhurandhar, Karthikeyan Ramamurthy, Kartik Ahuja and Vijay Arya. Generating Locally Invariant Explanations For Machine Learning. P202103521US01

• 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. P202200770US01

• Ronny Luss, Amit Dhurandhar and Miao Liu. Understanding Reinforcement Learning Policies by Identifying Strategic States. P202103551US01

• Amit Dhurandhar and Tejaswini Pedapati. Multiple Stage Knowledge Transfer. P202100730US01

• Saneem Ahmed Chemmengath, Amar Prakash Azad, Ronny Luss and Amit Dhurandhar. A System and Method for Generating Contrastive Explanations for Text Guided by Attributes. P202105758US01

• Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei and Kush Varshney. Interpretable Neural Network Architecture Using Continued Fractions . P202103523US01

• Vera Liao, Yunfeng Zhang, Jorge Andres Moros Ortiz, Amit Dhurandhar and Ronny Luss. A method to provide intelligent AI explanations by ranking different XAI techniques based on task contexts and user profiles. P202010062US01

• Zaid Tariq, Karthikeyan Ramamurthy, Dennis Wei and Amit Dhurandhar. Post-hoc Local Explanations of Black-Box Similarity Learners. P202006710

• Ronny Luss and Amit Dhurandhar. A System and Method for Generating Path of Minimally Sufficient Explanations for Improving Model Understanding. P202004697

• Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam and Amit Dhurandhar. Learning Global Transparent Models from Local Contrastive Explanations. P202003154

• Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney and Amit Dhurandhar. A System and Method for Learning Causal Representations and Invariant Predictors. P202001374

• Kartik Ahuja, Amit Dhurandhar, Karthikeyan Shanmugam and Kush Varshney. A System and Method for Initializing Optimization Solvers. P202001286

• Tsuyoshi Ide, Amit Dhurandhar, Jiri Navratil, Naoki Abe and Moninder Singh. Method and apparatus for diagnosing anomalies detected by black-box models. P202000529 (granted)

• Amit Dhurandhar, Karthikeyan Shanmugam and Ronny Luss. A System and Method that Leverages Simple Model Predictions for Enhancing its Performance. P201911468 (granted)

• Amit Dhurandhar, Pin-Yu Chen, Karthikeyan Shanmugam, Tejaswini Pedapati, Avinash Balakrishnan and Ruchir Puri. Contrastive Explanations Method for Differentiable and Non-Differentiable Black Box Models for Tabular Data. P201807487 (granted)

• Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss and Peder Olsen. A System and Method for Improving Simple Models using Confidence Profiles. P201805725 (granted)

• Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Karthikeyan Shanmugam and Payel Das. A System and Method for Contrastive Explanations for Interpreting Black Box Models. P201805724 (granted)

• Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri and Karthikeyan Shanmugam. Contrastive Explanations for Images with Monotonic Attribute Functions. P201900634US01 (granted)

• Amit Dhurandhar, Guillermo Cecchi and Pablo Meyer. Predicting Human Descriminability of Odor Mixtures. P201802948US01 (granted)

• Amit Dhurandhar, Sechan Oh and Marek Petrik. Interpretable Rule Generation using loss-preserving transformation. U.S. Provisional Patent Application Serial No. 15/489,418 (granted)

• Guillermo Cecchi, A. Dhurandhar, E. Guitterez and P. Meyer. Prediction of Olfactory and Taste Perception through Semantic Encoding. P20170003US01

• Guillermo Cecchi, Amit Dhurandhar, Stacey M Gifford, Raquel Norel, Pablo Meyer rojas, Kahn Rhrissorrakrai and Bo Zhang. Predicting User Preferences based on Olfactory Characteristics. YOR8020161333

• Ioana Baldini, Amit Dhurandhar, Abhishek Kumar, Aleksandra Mojsilovic, Kein T Pham, Kush R Varshney andMaja Vukovic. Humanitarian Crisis analysis using secondary information gathered by focused web crawler. YOR920161631 (granted)

• Guillermo Cecchi, Amit Dhurandhar and Pablo Meyer. Correlating Olfactory Perception with Molecular Structure. YOR920161332 (granted)

• Amit Dhurandhar, Bruce Graves, Rajesh Ravi and Markus Ettl. A System and Method for Identifying Procurement Fraud/Risk. U.S. Provisional Patent Application Serial No. 14/186,071

• Amit Dhurandhar, Stuart Seigal, Yada Zhu and Jayant Kalagnanam. A System and Method for Detecting Electricity Theft via Meter Tampering Using Statistical Methods of Anomaly Detection. U.S. Provisional Patent Application Serial No. 13/909,239 (granted)

• Amit Dhurandhar and Jun Wang. A System and Method for Relational Transductive Learning. U.S. Provisional Patent Application Serial No. 13/787,807 (granted)
  

• Pawan Chowdhary, Amit Dhurandhar, Markus Ettl, Soumyadip Ghosh, Bruce Graves, Bill Schaefer and Yu Tang. Method and system for optimizing procurement spend compliance. U.S. Provisional Patent Application Serial No. 13/339,626 (granted)

• Robert Baseman, Amit Dhurandhar, Sholom Weiss and Brian White. A System and Method for Continuous Prediction of Expected Chip Performance Throughout the Production Lifecycle. U.S. Provisional Patent Application Serial No. 13/242,692 (granted)

• Amit Dhurandhar. Improving Predictions using Aggregate Information. U.S. Provisional Patent Application Serial No. 13/184,000 (granted)

• Amit Dhurandhar and Jayant Kalagnanam. Multistep Time Series Prediction in Complex Instrumented Domains. U.S. Provisional Patent Application Serial No. 12/966,465 (granted)
  

• Amit Dhurandhar, Robert Baseman and Fateh Tipu. A System and Method for Identifying Significant Consumable Insensitive Trace Features. YOR8020140228 (granted)

• Amit Dhurandhar, Bruce Graves, Rajesh Ravi, Gopikrishnan Maniachari, Markus Ettl, Anthony Mazzatti. A Robust System for Ranking and Tracking Suspicious Procurement Entities. YOR8020140315