Search Publications

@conference{Manwani2023,
title = {Features Normalization and Standardization (FNS) - An Unsupervised approach for detecting Adversarial attacks for Medical Images},
author = {Sreenivasan M and Naresh Manwani},
url = {https://icaart.scitevents.org/home.aspx},
year = {2023},
date = {2023-02-28},
booktitle = {15th International Conference on Agents and Artificial Intelligence (ICAART)},
keywords = {adversarial training},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Maheshwara2022,
title = {RoLNiP: Robust Learning Using Noisy Pairwise Comparisons},
author = {Samartha S M and Naresh Manwani},
year = {2022},
date = {2022-12-15},
booktitle = {Proceedings of 14th Asian Conference on Machine Learning (ACML)},
keywords = {Multiclass, Pairwise Similarity Data, Robust Learning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Hima2022,
title = {Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems},
author = {Hima Patel and Shanmukha Guttula and Ruhi Sharma Mittal and Naresh Manwani and Laure Berti-Equille and Abhijit Manatkar},
url = {https://dl.acm.org/doi/pdf/10.1145/3534678.3542604
https://abhijitmanatkar.github.io/kdd22/},
year = {2022},
date = {2022-08-31},
booktitle = {KDD 2022},
keywords = {EDA},
pubstate = {published},
tppubtype = {conference}
}

@conference{Narendra2022,
title = {Journey to the center of the words: Word weighting scheme based on the geometry of word embeddings},
author = {Narendra Babu Unnam and P Krishna Reddy and Amit Pandey and Naresh Manwani},
year = {2022},
date = {2022-07-08},
booktitle = {34th International Conference on Scientific and Statistical Database Management (SSDBM)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Mudit2022,
title = {ALBIF: Active Learning with BandIt Feedbacks},
author = {Mudit Agarwal and Naresh Manwani},
year = {2022},
date = {2022-05-19},
booktitle = {26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{Srenivasan2022,
title = {Momentum Iterative Gradient Sign Method outperforms PGD Attacks},
author = {Sreenivasan Mohandas and Naresh Manwani and Durga Prasad Dhulipudi},
year = {2022},
date = {2022-02-05},
booktitle = {14th International Conference on Agents and Artificial Intelligence (ICAART)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{Gaurav2021,
title = {Multiclass Classification using Dilute Bandit Feedback},
author = {Gaurav Batra and Naresh Manwani},
year = {2021},
date = {2021-11-12},
booktitle = {PRICAI 2021},
keywords = {Bandit Feedback, multiclass classification, online learning},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Sarath-et-al-2021,
title = {The Curious Case of Convex Networks},
author = {Sarath Sivaprasad and Ankur Singh and Naresh Manwani and Vineet Gandhi},
year = {2021},
date = {2021-09-17},
booktitle = {ECML 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Bhavya-Naresh2021,
title = {RISAN: Robust Instance Specific Deep Abstention Network},
author = {Bhavya Kalra and Naresh Manwani},
year = {2021},
date = {2021-07-29},
booktitle = {UAI 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Mudit2021,
title = {Learning Multiclass Classifier Under Noisy Bandit Feedback},
author = {Mudit Agarwal and Naresh Manwani},
year = {2021},
date = {2021-05-15},
booktitle = {PAKDD 2021},
keywords = {Bandit Feedback, Label Noise, multiclass classification, online learning},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Maanik2021_CODS-COMAD,
title = {Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels},
author = {Maanik Arora and Naresh Manwani},
year = {2021},
date = {2021-01-10},
booktitle = {CoDS-COMAD 2021},
keywords = {multiclass classification, online learning},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Maanik2020_ACML,
title = {Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks},
author = {Maanik Arora and Naresh Manwani},
url = {http://proceedings.mlr.press/v129/arora20a.html},
year = {2020},
date = {2020-11-30},
booktitle = {ACML 2020},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{Bhanu2020_ACML,
title = {Robust Deep Ordinal Regression under Label Noise},
author = {Bhanu Garg and Naresh Manwani},
url = {http://proceedings.mlr.press/v129/garg20a.html},
year = {2020},
date = {2020-11-30},
booktitle = {ACML 2020},
keywords = {Label Noise, Ordinal Regression},
pubstate = {published},
tppubtype = {conference}
}

@conference{Kulin2020,
title = {Online Active Learning of Reject Option Classifiers},
author = {Kulin Shah and Naresh Manwani},
url = {https://arxiv.org/abs/1906.06166},
year = {2020},
date = {2020-02-07},
booktitle = {AAAI},
publisher = {AAAI Press},
keywords = {ActiveLearning, Online, RejectOption},
pubstate = {forthcoming},
tppubtype = {conference}
}

@conference{Himanshu_CoDS2020,
title = {Robust Learning of Multi-Label Classifiers under Label Noise},
author = {Himanshu Kumar and Naresh Manwani and P.S. Sastry},
doi = {https://doi.org/10.1145/3371158.3371169},
year = {2020},
date = {2020-01-07},
booktitle = {ACM India Joint International Conference on Data Science & Management of Data},
pages = {90-97},
abstract = {In this paper, we address the problem of robust learning of multi-label classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and a surrogate of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on the robust learning of multi-label classifiers under label noise.},
keywords = {Label Noise, Multi-Label, Robust},
pubstate = {published},
tppubtype = {conference}
}