Rajeev Verma
I'm an ELLIS PhD student at AMLab / Delta Lab supervised by Eric Nalisnick and Christian A. Naesseth. Previously, I studied Electrical Engineering at the Indian Institute of Technology Patna (IITP) and Artificial Intelligence at the University of Amsterdam (UvA).
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Research Interests
I'm generally interested in bridging the gap between prediction and decision-making, especially in the context of the institutional separation between model designers and decision-makers. I'm also interested in safe statistics, imprecise probabilities, and possibility theory.
Previously, I worked on studying the calibration properties of learning to defer (L2D) systems [ICML'22], extending L2D systems to allow for multiple experts [AISTATS'23], and studying the out-of-distribution behavior of L2D systems (in preparation). I also collaborated on a project on the test-time adaption of L2D to new experts [AISTATS'24].
Blog
Selected Publications
(* Denotes equal contribution)
- Alexander Timans*, Rajeev Verma*, Eric Nalisnick, Christian A. Naesseth.
On Continuous Monitoring of Risk Violations under Unknown Shift.
To appear at UAI 2025.
- Rajeev Verma, Volker Fischer, Eric Nalisnick.
On Calibration in Multi-Distribution Learning.
To appear at ACM FAccT 2025.
Note: Also gave an invited talk at the 2nd Workshop on Learning Under Weakly Structured Information.
- Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric Nalisnick.
Learning to Defer to a Population: A Meta-Learning Approach.
In Twenty-seventh Conference on Artificial Intelligence and Statistics (AISTATS 2024).
[Oral, Student paper award (top 1%)].
- Rajeev Verma*, Daniel Barrejón*, Eric Nalisnick.
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles.
In Twenty-sixth Conference on Artificial Intelligence and Statistics (AISTATS 2023).
Note: Also appeared at the ICML 2022 Workshop on Human-Machine Collaboration and Teaming as On the Calibration of Learning to Defer to Multiple Experts.
- Rajeev Verma, Eric Nalisnick.
Calibrated Learning to Defer with One-vs-All Classifiers.
In International Conference on Machine Learning (ICML 2022).
- Rajeev Verma.
On the Calibration of Learning to Defer Systems.
Master's Thesis 2022 (UvA).
Service
Reviewer:
ICML: 2023-2025
NeurIPS: 2023 (Top reviewer)
UAI: 2024-2025
ICLR: 2023
ACL ARR: 2024, 2025
Teaching:
Human-in-the-Loop Machine Learning (Teaching Assistant)
Deep Learning 2 (Teaching Assistant)