Neelabh Madan

I am a first year Computer Science Ph.D. student at NYU, Courant. I am advised by Lakshmi Subramanian .

Previously, I worked as a Research Fellow at Microsoft Research, India (MSRI), where I was advised by Dr. Manik Varma and Dr. Amit Sharma. Here I closely worked with the Ads Recommendation Team under Wenhao Lu and Ahskay Soni.

I graduated from Indian Insitute of Technology (IIT) Delhi with a Bachelor's in Mechanical Engineering and a minor degree in Computer Science. During my undergraduate, I worked with Prof. Chetan Arora.

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Research Interests

Inspired from how humans learn, I am interested in developing practical algorithms and architectures that enable machine learning (ML) systems to correct themselves and learn on the fly from expert feedback. I am also interested in how we can ensure models learn robust rules for applications with a large user base.

Publications
* denotes equal contribution, + denotes signifcant contribution


Leveraging Domain-Specific Rules In Deep Learning Models
Ananth Balashankar, Ankit Bhawdwaj, Neelabh Madan+, Thomas Wies, Lakshmi Subramanian ,
Under Review: AISTATS 2025
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Anirudh Buvanesh*, Rahul Chand*, Jatin Prakash*, Bhawna Paliwal, Mudit Dhawan, Neelabh Madan, Deepesh Hada, Yashoteja Prabhu, Manik Varma,
ICLR 2024
Paper / Code
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
Neelabh Madan*, Ramya Hebbalaguppe*, Jatin Prakash*, Chetan Arora
CVPR 2022 Oral (4.2% acceptance rate)
Paper / Code