Amit Dhurandhar

According to our database, Amit Dhurandhar authored at least 94 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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., December, 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

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

CoRR, 2024

Identifying Sub-networks in Neural Networks via Functionally Similar Representations.

Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Dennis Wei

CoRR, 2024

Programming Refusal with Conditional Activation Steering.

Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Erik Miehling, Pierre L. Dognin, Manish Nagireddy, 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

CELL your Model: Contrastive Explanation Methods for Large Language Models.

Ronny Luss, Erik Miehling, Amit Dhurandhar

CoRR, 2024

Large Language Model Confidence Estimation via Black-Box Access.

Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri

CoRR, 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

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

CoRR, 2024

Trust Regions for Explanations via Black-Box Probabilistic Certification.

Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization.

Naiyu Yin, Hanjing Wang, Yue Yu, Tian Gao, Amit Dhurandhar, Qiang Ji

Proceedings of the Computer Vision - ECCV 2024, 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

Proceedings of the Findings of the Association for Computational Linguistics, 2024

Ranking Large Language Models without Ground Truth.

Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Atomist or holist? A diagnosis and vision for more productive interdisciplinary AI ethics dialogue.

Travis Greene, Amit Dhurandhar, Galit Shmueli

Patterns, January, 2023

Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning.

Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Kartik Ahuja, Vijay Arya

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 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

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Reprogramming Pretrained Language Models for Antibody Sequence Infilling.

Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Explainable Cross-Topic Stance Detection for Search Results.

Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans, Nava Tintarev

Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, 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

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Local Explanations for Reinforcement Learning.

Ronny Luss, Amit Dhurandhar, Miao Liu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

Patterns, 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

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

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

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners.

Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq

CoRR, 2022

Auto-Transfer: Learning to Route Transferrable Representations.

Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations.

Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Auto-Transfer: Learning to Route Transferable Representations.

Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Proceedings of the Tenth International Conference on Learning Representations, 2022

Multihop: Leveraging Complex Models to Learn Accurate Simple Models.

Amit Dhurandhar, Tejaswini Pedapati

Proceedings of the IEEE International Conference on Knowledge Graph, 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

Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

Let the CAT out of the bag: Contrastive Attributed explanations for Text.

Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Building Accurate Simple Models with Multihop.

Amit Dhurandhar, Tejaswini Pedapati

CoRR, 2021

Towards Better Model Understanding with Path-Sufficient Explanations.

Ronny Luss, Amit Dhurandhar

CoRR, 2021

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions.

Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Leveraging Latent Features for Local Explanations.

Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective.

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

Proceedings of the 9th International Conference on Learning Representations, 2021

Treatment Effect Estimation Using Invariant Risk Minimization.

Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar

Proceedings of the IEEE International Conference on Acoustics, 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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD, 2021

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

Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Anomaly Attribution with Likelihood Compensation.

Tsuyoshi Idé, Amit Dhurandhar, Jirí Navrátil, Moninder Singh, Naoki Abe

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

J. Mach. Learn. Res., 2020

Learning to Initialize Gradient Descent Using Gradient Descent.

Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney

CoRR, 2020

Learning Global Transparent Models from Local Contrastive Explanations.

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

CoRR, 2020

Model Agnostic Multilevel Explanations.

Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Global Transparent Models consistent with Local Contrastive Explanations.

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Enhancing Simple Models by Exploiting What They Already Know.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

Proceedings of the 37th International Conference on Machine Learning, 2020

Invariant Risk Minimization Games.

Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar

Proceedings of the 37th International Conference on Machine Learning, 2020

Classifier Invariant Approach to Learn from Positive-Unlabeled Data.

Amit Dhurandhar, Karthik S. Gurumoorthy

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the FAT* '20: Conference on Fairness, 2020

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

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

Model Agnostic Contrastive Explanations for Structured Data.

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

CoRR, 2019

Leveraging Simple Model Predictions for Enhancing its Performance.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

CoRR, 2019

Generating Contrastive Explanations with Monotonic Attribute Functions.

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

CoRR, 2019

Efficient Data Representation by Selecting Prototypes with Importance Weights.

Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi, Charu C. Aggarwal

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

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

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

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

Karthik S. Gurumoorthy, Amit Dhurandhar

CoRR, 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

Improving Simple Models with Confidence Profiles.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Supervised item response models for informative prediction.

Tsuyoshi Idé, Amit Dhurandhar

Knowl. Inf. Syst., 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

ProtoDash: Fast Interpretable Prototype Selection.

Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo A. Cecchi

CoRR, 2017

A Formal Framework to Characterize Interpretability of Procedures.

Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam

CoRR, 2017

TIP: Typifying the Interpretability of Procedures.

Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam

CoRR, 2017

Learning with Changing Features.

Amit Dhurandhar, Steve Hanneke, Liu Yang

CoRR, 2017

Uncovering Group Level Insights with Accordant Clustering.

Amit Dhurandhar, Margareta Ackerman, Xiang Wang

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

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

Building an Interpretable Recommender via Loss-Preserving Transformation.

Amit Dhurandhar, Sechan Oh, Marek Petrik

CoRR, 2016

2015

Improving classification performance through selective instance completion.

Amit Dhurandhar, Karthik Sankaranarayanan

Mach. Learn., 2015

Bounds on the moments for an ensemble of random decision trees.

Amit Dhurandhar

Knowl. Inf. Syst., 2015

Big Data System for Analyzing Risky Procurement Entities.

Amit Dhurandhar, Bruce Graves, Rajesh Kumar Ravi, Gopikrishnan Maniachari, Markus Ettl

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Informative Prediction Based on Ordinal Questionnaire Data.

Tsuyoshi Idé, Amit Dhurandhar

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Robust System for Identifying Procurement Fraud.

Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Efficient and accurate methods for updating generalized linear models with multiple feature additions.

Amit Dhurandhar, Marek Petrik

J. Mach. Learn. Res., 2014

Symmetric Submodular Clustering with Actionable Constraint.

Amit Dhurandhar, Karthik S. Gurumoorthy

CoRR, 2014

2013

Probabilistic characterization of nearest neighbor classifier.

Amit Dhurandhar, Alin Dobra

Int. J. Mach. Learn. Cybern., 2013

Single Network Relational Transductive Learning.

Amit Dhurandhar, Jun Wang

J. Artif. Intell. Res., 2013

Using coarse information for real valued prediction.

Amit Dhurandhar

Data Min. Knowl. Discov., 2013

Improving quality control by early prediction of manufacturing outcomes.

Sholom M. Weiss, Amit Dhurandhar, Robert J. Baseman

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Intelligently querying incomplete instances for improving classification performance.

Karthik Sankaranarayanan, Amit Dhurandhar

Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013

2012

Distribution-free bounds for relational classification.

Amit Dhurandhar, Alin Dobra

Knowl. Inf. Syst., 2012

2011

Improving predictions using aggregate information.

Amit Dhurandhar

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Managing Procurement Spend Using Advanced Compliance Analytics.

Pawan Chowdhary, Markus Ettl, Amit Dhurandhar, Soumyadip Ghosh, Gopikrishnan Maniachari, Bruce Graves, Bill Schaefer, Yu Tang

Proceedings of the IEEE 8th International Conference on e-Business Engineering, 2011

2010

Multi-step Time Series Prediction in Complex Instrumented Domains.

Amit Dhurandhar

Proceedings of the ICDMW 2010, 2010

Learning Maximum Lag for Grouped Graphical Granger Models.

Amit Dhurandhar

Proceedings of the ICDMW 2010, 2010

2009

Semi-analytical method for analyzing models and model selection measures based on moment analysis.

Amit Dhurandhar, Alin Dobra

ACM Trans. Knowl. Discov. Data, 2009

2005

Robust Pattern Recognition Scheme for Devanagari Script.

Amit Dhurandhar, Kartik Shankarnarayanan, Rakesh Jawale

Proceedings of the Computational Intelligence and Security, International Conference, 2005