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description=Rajeev Verma is an ELLIS PhD student at AMLab/Delta Lab (University of Amsterdam). Research interests include bridging prediction and decision-making, calibration, safe statistics, imprecise probabilities, and possibility theory.;

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rajeev verma phd student ellis amlab delta lab uva home publications cv essays service scholar rajeev verma phd student amlab delta lab university of amsterdam rajeev ee15 gmail com github twitter linkedin i m an ellis phd student at the university of amsterdam uva where i m a part of the amlab and delta lab previously i studied electrical engineering at the indian institute of technology patna iitp and artificial intelligence at the university of amsterdam uva 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 i bridge philosophy mathematics and policy essays notes from the underground a running blog of my most random thoughts on under determinism no sure loss calibration and insurance what are good forecasts a relativistic perspective of uncertainty in machine learning note a part of this appeared at the icml 2026 workshop on philosophy meets machine learning what counts as trustworthy as a relativistic perspective of reliability in machine learning that was invited for an in person oral talk selected publications denotes equal contribution ongoing workshop articles avoiding the tragedy of the commons in ai regulation via dynamic licensing rajeev verma anurag singh christian a naesseth eric nalisnick krikamol muandet iclr 2026 workshop on ai for mechanism design and strategic decision making aims so what are good imprecise forecasts rajeev verma rabanus derr christian a naesseth volker fischer eric nalisnick workshop on epistemic intelligence in machine learning eurips 2025 conference papers thesis boosting for predictive sufficiency abbavaram gowtham reddy rajeev verma celia rubio madrigal krikamol muandet rebekka burkholz iclr 2026 on continuous monitoring of risk violations under unknown shift alexander timans rajeev verma eric nalisnick christian a naesseth uai 2025 talk on calibration in multi distribution learning rajeev verma volker fischer eric nalisnick acm facct 2025 note also gave an invited talk at the 2nd workshop on learning under weakly structured information learning to defer to a population a meta learning approach dharmesh tailor aditya patra rajeev verma putra manggala eric nalisnick aistats 2024 oral student paper award top 1 learning to defer to multiple experts consistent surrogate losses confidence calibration and conformal ensembles rajeev verma daniel barrejón eric nalisnick 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 calibrated learning to defer with one vs all classifiers rajeev verma eric nalisnick icml 2022 on the calibration of learning to defer systems rajeev verma master s thesis 2022 uva talk uva news service reviewer icml gold reviewer 2026 neurips top reviewer 2023 uai iclr acl teaching human in the loop machine learning teaching assistant deep learning 2 teaching assistant machine learning 2 teaching assistant design adapted from the template by gregory gundersen