About Me
I am a Computer Science PhD candidate at the University of Wisconsin-Madison. My advisors are Prof. Frederic Sala and Prof. Ramya Korlakai Vinayak . I am broadly interested in foundations of machine learning, statistics and applications. Before coming to Madison I was at IBM Research, Bangalore for 3 years. I spent two wonderful years at the IISc, Bangalore to obtain a masters in CS, where I worked with Prof. Chiranjib Bhattacharyya. I grew up playing cricket in a village in Bundelkhand, M.P., India and graduated from SGSITS, Indore with B.E. in computer science. For more details you can checkout my CV.
Actively seeking industry research/post-doc positions with start date in summer/fall'25.
Research Interests
- My current interests revolve around developing human-in-the-loop solutions for safe deployments of ML/AI solutions and labeled data acquisition with minimal supervision. Towards these goals I have worked on selective classification, calibration, conformal prediction, weak supervision, non-euclidean geometry, active learning, self-training and safe anytime valid inference.
- I am enthusiastic about game theory, data and parameter markets, multi-agent/distributed/federated learning settings.
- I have enjoyed working on knowledge graphs, information retrieval and reasoning.
News
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[12/24]: Excited to be at NeurIPS from 9 to 16th Dec 🍁. Presenting the following papers. See you there! 😎
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[C1] My work on improving auto-labeling with learnable scores, with Reid, Jiet, Srinath, Fred and Ramya.
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[C2] Changho's work on effortless adaptation of zero-shot models with Jitian, Sonia and Fred.
- [W1] My work on improving LLM's decision making with conformal prediction with Alan and JPMorgan AI folks. In the workshops on Statistics in LLM and Open-World Agents.
- [W2] My work on fixing the score and thresholding problem in pseudo-labeling based SSL with Reid, Srinath, Jiet and advisors. In the workshops on Self-Supervised Learning.
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[C1] My work on improving auto-labeling with learnable scores, with Reid, Jiet, Srinath, Fred and Ramya.
- [10/24] Two papers accepted in NeurIPS'24! 🎉 🎉.
- [07/24] Papers on auto-labeling and pseudo-labeling based SSL in the DMLR workshop at ICML.
- [06/24] Excited to intern at JPMorgan AI Research, NYC with Alan Mishler and Nic Dalmasso.
- [04/24] In Valencia to present our work on OOD detection with FPR control in AISTATS. [Twitter]
- [04/24] New preprint showing how we improve confidence functions for auto-labeling.
- [01/24] Paper with Lin and Ramya on OOD Detection with FPR control got accepted in AISTATS 2024! 🎉.
- [11/23] Presented our work on auto-labeling in MLOPT Idea Seminar. [Slides].
- [10/23] Presented our work on Taming False Positives in Out of Distribution Detection at IFDS Seminar.
- [09/23] Two papers accepted in NeurIPS! 🎉 🎉.
- Checkout our work on fundamentals of auto-labeling got accepted as a spotlight .
- Checkout our work on foundations of parameter markets.
- [07/23] Presented our work on auto-labeling and OOD detection in DMLR and AI-HCI workshops @ ICML'23. [Twitter]
- [06/23] New blog post on aggregating LLM or foundation model objects using weak supervision.
Checkout my work with amazing collaborators. I'd love to know any feedback you might have on any of these works. Always open to chat and collaborate! so feel free to reach out :)
Research Papers
F = Full/Conference Paper, S = Short/Workshop Paper
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs
Harit Vishwakarma, Alan Mishler, Thomas Cook, Niccolò Dalmasso, Natraj Raman, Sumitra Ganesh
NeurIPS 2024 workshop on Statistical Frontiers in LLMs and Foundation Models, 2024
NeurIPS 2024 Workshop on Open-World Agents, 2024
Under Review, 2024
Improving FPR Control in OOD Detection with Learnable Score Functions and Human-in-the-Loop
Daisuke Yamada, Harit Vishwakarma, Ramya Korlakai Vinayak
Under Review, 2024
PabLO: Improving Semi-Supervised Learning with Pseudolabeling Optimization
Harit Vishwakarma, Reid (Yi) Chen*, Srinath Namburi*, Sui Jiet Tay, Ramya K. Vinayak, Frederic Sala
NeurIPS 2024 workshop on Self-Supervised Learning - Theory and Practice, 2024
ICML 2024 Workshop on Data-centric Machine Learning (DMLR), 2024
Under Review, 2024
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
Harit Vishwakarma, Reid (Yi) Chen, Sui Jiet Tay, Srinath Namburi, Frederic Sala, Ramya Korlakai Vinayak
Neural Information Processing Systems (NeurIPS), 2024
OTTER: Improving Zero-Shot Classification via Optimal Transport
Changho Shin, Jitian Zhao, Sonia Cromp, Harit Vishwakarma, Frederic Sala
Neural Information Processing Systems (NeurIPS), 2024
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Promises and Pitfalls of Threshold-based Auto-labeling
Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
Neural Information Processing Systems (NeurIPS), 2023 (Spotlight)
ScriptoriumWS: A Code Generation Assistant for Weak Supervision
Tzu-Heng Huang, Catherine Cao, Spencer Schoenberg, Harit Vishwakarma, Nicholas Roberts, Frederic Sala
Deep Learning for Code (DL4C) Workshop at ICLR, 2023
Train 'n Trade: Foundations of Parameter Markets
Tzu-Heng Huang, Harit Vishwakarma, Frederic Sala
Neural Information Processing Systems (NeurIPS), 2023
Lifting Weak Supervision To Structured Prediction
Harit Vishwakarma, Nicholas Roberts, Frederic Sala
Neural Information Processing Systems (NeurIPS), 2022
Universalizing Weak Supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala
International Conference on Learning Representations (ICLR), 2022
Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos
Neural Information Processing Systems (NeurIPS), 2020 (Spotlight)
Attack of the Tails: Yes, You Really can Backdoor Federated Learning
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, D. Papailiopoulos
Neural Information Processing Systems (NeurIPS), 2020
Quantum Embedding of Knowledge for Reasoning
D. Garg, S. Ikbal, S. K Srivastava, H. Vishwakarma, H. Karnam, L. V. Subramaniam
Neural Information Processing Systems (NeurIPS), 2019
Know Thy Neighbors, and More! Studying the Role of Context in Entity Recommendation
Sumit Bhatia, Harit Vishwakarma
ACM Conference on HyperText and Social Media (HT), 2018 (Best Paper Nominee)
An End-to-end Machine Learning Pipeline that Ensures Fairness Policies
S. Shaikh, H. Vishwakarma, S. Mehta, K. R. Varshney, K. N. Ramamurthy, D. Wei
Bloomberg Data for Good Exchange, 2017
Service
Served as a reviewer for,
- Neural Information Processing Systems (NeurIPS) 2021, 2022, 2023, 2024. Top Reviewer (~ top 8%) for NeurIPS 2022.
- International Conference on Machine Learning (ICML) 2022, 2023, 2024.
- International Conference on Learning Representations (ICLR) 2021, 2022, 2023, 2024, 2025.
- International Conference on Artificial Intelligence and Statistics (AISTATS) 2024, 2025.
- Association for the Advancement of Artificial Intelligence, Conference (AAAI) 2025.
- Transactions on Machine Learning Research Journal (TMLR) Since 2022.
Teaching
- I was TA for,
- 1. CS 761, advanced course on Mathematical Foundations of ML, Instructor Prof. Rob Nowak, [Spring, 22].
I helped with homeworks, exams, held office hours and gave a lecture on uniform convergence results for finite hypothesis class.
- 2. CS 760, grad level intro to ML course, Instructor Prof. Fred Sala, [Fall, 21 ]. I helped with homeworks, exams and held office hours.
- I have taught students at various levels in my village during my high school, partly to support my education and with the hope to get them interested.
Miscellaneous
- I enjoy sports/athletic activities, particularly enjoy playing cricket, badminton, racquetball and biking, running.
- I like reading Indian philosophy and Hindi poetry (e.g. Kabir).