Book Chapters

1. Robust Learning of Classifiers in the Presence of Label Noise

   1. • P. S. Sastry, Naresh Manwani

      • Pattern Recognition and Big Data, World Scientific Publishers, pages: 167-197, 2017

      • [Published Version]

Journal Papers

1. Exact Passive-Aggressive Algorithms for Ordinal Regression Using Interval Labels

   1. • Naresh Manwani, Mohit Chandra

      • IEEE Transactions on Neural Networks and Learning Systems, volume: 31, issue: 9, pages: 3259-3268, September 2020

      • [Published Version]

2. Making Risk Minimization Tolerant to Label Noise

   1. • Aritra Ghosh, Naresh Manwani, P. S. Sastry

      • Neurocomputing Elsevier, volume: 160, pages: 93-107, July 2015

      • [Published Version][arXiv Version]

3. K-Plane Regression

   1. • Naresh Manwani, P. S. Sastry

      • Information Sciences, Elsevier, volume: 292, pages: 39-56, January, 2015

      • [Published Version]

4. Noise Tolerance under Risk Minimization

   1. • Naresh Manwani, P. S. Sastry

      • IEEE Transactions on Cybernetics, volume: 43,  issue: 3, pages: 1146-1151, March, 2013

      • [Published Version][arXiv Version]

5. Geometric Decision Tree

   1. • Naresh Manwani, P. S. Sastry

      • IEEE Transactions on Systems, Man and Cybernetics, Part--B, volume: 42, issue: 1, pages: 181-192, February, 2012

      • [Published Version][arXiv Version]

6. A Team of Continuous Action Learning Automata for Noise Tolerant Learning of Half-Spaces

   1. • P. S. Sastry, G. D. Nagendra, Naresh Manwani

      • IEEE Transactions on Systems, Man and Cybernetics, Part--B, volume: 40, issue: 1, pages: 19-28, February, 2010

      • [Published Version] [Codes]

Conference/Workshop Papers

1. Node Classification With Integrated Reject Option For Legal Judgement Prediction

   • • Uday Bhaskar K, Jayadratha Gayen, Charu Sharma, Naresh Manwani

     • Accepted for presentation at Deployable AI (DAI) Workshop at AAAI 2025

     • [arXiv]

2. Towards Calibrated Losses for Adversarial Robust Reject Option Classification

   • • Vrund Shah, Tejas Chaudhuri, Naresh Manwani

     • ACML 2024

     • Received the Best Student Paper Runners-Up Award

     • [arXiv][GitHub Link for Code][Openreview]

3. Equiscale: Equitable Scaling for Abstention Learning

   • • Tejas Choudhury, Naresh Manwani

     • PRICAI 2024

     • [Code][Paper Link]

4. ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis.

   • • Abhijit Manatkar, Devarsh Patel, Hima Patel, Naresh Manwani

     • AIML Systems 2024

     • Received the Research Track Best Paper Runners-Up Award 

     • [Slides][Video][Code][arXiv]

5. Text Representation Models based on the Spatial Distributional Properties of Word Embeddings

   • • Narendra Babu Unnam, Krishna Reddy Polepalli, Amit Pandey, Naresh Manwani

     • Young Research Symposium, CoDS-COMAD, 2024.

6. CDAN: Cost Dependent Deep Abstention Network

   • • Bhavya Kalra and Naresh Manwani

     • PRICAI 2023

     • [Link][Slides]

7. RPL-SVM: Making SVM Robust Against Missing Values and Partial Labels

   • • Sreenivasan M and Naresh manwani

     • PRICAI 2023

     • [Link][Slides]

8. Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks

   • • Naresh Manwani, Mudit Agarwal

     • IJCNN 2023

     • [arXiv][Code][Poster][Published Version]

9. Features Normalization and Standardization (FNS) - An Unsupervised approach for detecting Adversarial attacks for Medical Images

   • • Sreenivasan M and Naresh Manwani

     • ICAART 2023

     • [Link]

10. RoLNiP: Robust Learning Using Noisy Pairwise Comparisons

    • • Samartha S M, Naresh Manwani

      • 14th Asian Conference on Machine Learning (ACML 2022), Hyderabad, India

      • [Published Version][Code][Slides][arXiv]

11. Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems

    • • Hima Patel, Shanmukha Guttula, Ruhi Sharma Mittal, Naresh Manwani, Laure Berti-Equille, Abhijit Manatkar

      • Tutorial Presented at KDD 2022

      • [Link][Tutorial Website]

12. Journey to the center of the words: Word weighting scheme based on the geometry of word embeddings

    • • Narendra Babu Unnam, P Krishna Reddy, Amit Pandey and Naresh Manwani

      • 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022), Copenhagen, Denmark.

      • [Link]

13. ALBIF: Active Learning With BandIt Feedbacks

    • • Mudit Agarwal, Naresh Manwani

      • 26th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022), Chengdu, China.

      • [Code][Published Version]

14. Momentum Iterative Gradient Sign Method outperforms PGD Attacks

    • • Sreenivasan Mohandas, Naresh Manwani, Durga Prasad Dhulipudi

      • ICAART 2022

      • [Published Version]

15. Multiclass Classification using Dilute Bandit Feedback

    • • Gaurav Batra, Naresh Manwani

      • PRICAI 2021

      • [axXiv Version][Published Version][Code][Slides]

16. The Curious Case of Convex Networks

    • • Sarath Sivaprasad, Ankur Singh, Naresh Manwani, Vineet Gandhi

      • ECML-PKDD 2021

      • [arXiv Version][Published Version][Video Presentation]

17. RISAN: Robust Instance Specific Deep Abstention Network

    • • Bhavya Kalra, Naresh Manwani

      • UAI 2021

      • [arXiv Version][Published Version][Code][Video Presentation]

18. Cooperative Monitoring of Malicious Activity in Stock Exchanges

    • • Bhavya Kalra, Sai Krishna Munnangi, Kushal Majmundar, Naresh Manwani and Praveen Paruchuri

      • Workshop on Data Assessment and Readiness for Artificial Intelligence, PAKDD 2021

      • [Published Version]

19. Learning Multiclass Classifier Under Noisy Bandit Feedback

    • • Mudit Agarwal, Naresh Manwani

      • PAKDD 2021

      • [arXiv Version][Published Version][Code][Slides]

20. Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels

    • • Maanik Arora, Naresh Manwani

      • CoDS-COMAD 2021

      • [Published Version][Codes][Video Presentation]

21. Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks

    • • Maanik Arora, Naresh Manwani

      • ACML 2020

      • [Published Version][Video Presentation][Codes]

22. Robust Deep Ordinal Regression under Label Noise

    • • Bhanu Garg, Naresh Manwani

      • ACML 2020

      • [Published Version] [arXiv Version] [Codes][Video Presentation]

23. Online Algorithms for Multiclass Classification using Partial Labels

    • • Rajarshi Bhattacharjee, Naresh Manwani

      • PAKDD 2020

      • [arXiv Version] [Published Version] [Slides] [Codes][Video Presentation]

24. Online Active Learning of Reject Option Classifiers

    • • Kulin Shah, Naresh Manwani

      • 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA

      • [Published Version] [arXiv Version]

25. Robust Learning of Multi-Label Classifiers under Label Noise

    • • Himanshu Kumar, Naresh Manwani, P.S. Sastry

      • ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2020), Hyderabad, India

      • [Published Version] [Slides]

26. PRIL: Perceptron Ranking Using Interval Labels

    • • Naresh Manwani

      • ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2019), Kolkata, India

      • [arXiv Version] [Published Version] [Slides] [Codes]

27. Sparse Reject Option Classifier using Successive Linear Programming

    • • Kulin Shah, Naresh Manwani

      • 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, USA.

      • Received student travel grants from Microsoft Research India and Google Research India.

      • [arXiv Version]  [Published Version][Code]

28. Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts

    • • Subba Reddy Oota, Naresh Manwani, Raju S. Bapi

      • Presented at Visually-Grounded Interaction and Language (ViGIL), NeurIPS workshop 2018

      • [arXiv Version]

29. fMRI Semantic Category Decoding using Linguisitic Encoding of Word2Vec

    • • Subba Reddy Oota, Naresh Manwani, Raju S. Bapi

      • 25th International Conference on Neural Information Processing (ICONIP 2018), Siam Reap, Combodia.

      • [Published Version]

30. fMRI Semantic Category Decoding using Linguisitic Encoding of Word2Vec

    • • Subba Reddy Oota, Naresh Manwani, Raju S. Bapi

      • Presented at Visually-Grounded Interaction and Language (ViGIL), NIPS workshop 2017

      • [arXiv Version]

31. On the Robustness of Decision Tree Learning under Label Noise

    • • Arita Ghosh, Naresh Manwani, P.S. Sastry

      • 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Jeju, South Korea. 

      • [Published Version] [Slides]

32. Double Ramp Loss Based Reject Option Classifier

    • • Naresh Manwani, Kalpit Desai, Sanand Sasidharan, Ramasubramanian Sundararajan

      • 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Ho Chi Minh City, Viet Nam.

      • [Published Version]

33. Polyceptron: A Polyhedral Learning Algorithm

    • • Naresh Manwani, P. S. Sastry

      • Centenery Conference-Electrical Engineering (CCEE), IISc Bangalore, December 2011

      • [arXiv Version] [Slides][Codes]

34. Learning Polyhedral Classifiers using Logistic Function

    • • Naresh Manwani, P. S. Sastry

      • 2nd Asian Conference on Machine Learning (ACML), Tokyo, Japan, November 2010

      • [Published Version] [Slides]

35. A Geometric Algorithm for Learning Oblique Decision Trees

    • • Naresh Manwani, P. S. Sastry

      • 3rd International Conference on Pattern Recognition and Machine Intelligence (PReMI), New Delhi, India, December 2009.

      • [Published Version] [Slides]

36. Spoken Language Identification for Indian Languages using Split and Merge EM Algorithm

    • • Naresh Manwani, Suman K. Mitra, M. V. Joshi

      • 2nd International Conference on Pattern Recognition and Machine Intelligence (PReMI), Kolkata, India, December 2007

      • [Published Version]

Unpublished Articles on ArXiv

1. Plume: Polyhedral learning using mixture of experts

   1. 1. • Kulin Shah, P. S. Sastry, Naresh Manwani

         • [arXiv Link]

2. Pseudo labelling meets label smoothing for noisy partial label learning 

   1. 1. • Darshana Saravanan, Naresh Manwani, Vineet Gandhi

         • [arXiv Link]

PhD Thesis

• • • Thesis Title: Supervised Learning of Piecewise Linear Models

    • Advisor: Prof. P. S. Sastry, EE, IISc Bangalore

    • [Copy of Thesis]