Research Interests
I am interested in developing practical methods that make machine learning (ML) systems robust, especially to naturally occurring distribution changes, so that they can be deployed reliably in the real world at large-scales.
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Publications
* denotes equal contribution, + denotes signifcant contribution
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A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
Ramya Hebbalaguppe*,
Jatin Prakash*,
Neelabh Madan*,
Chetan Arora
CVPR 2022 Oral (4.2% acceptance rate)
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Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Anirudh Buvanesh*,
Rahul Chand*,
Jatin Prakash+,
Bhawna Paliwal ...,
Manik Varma
ICLR 2024
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Reviews
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Renee: End-to-end training of extreme classification models
Vidit Jain,
Jatin Prakash,
Deepak Saini,
Jian Jiao,
Ramachandran Ramjee,
Manik Varma
MLSys 2023
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A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions
Ramya Hebbalaguppe,
Soumya Suvra Ghosal,
Jatin Prakash,
Harshad Khadilkar,
Chetan Arora
ECML-PKDD 2022
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