Hi! PARIS is pleased to propose an exceptional seminar of Aaditya Ramdas.
Thanks to Aymeric Dieuleveut, Hi! PARIS Fellowship holder 2021 and Assistant Professor in Statistics at École polytechnique, we have the pleasure to welcome Aaditya Ramdas for a proposed seminar, entitled “Conformal prediction beyond exchangeability “.
Monday 23 May 2022, 11am – 12.30pm
Telecom Paris, Room 0A128
On site + Zoom (registration link)
Conformal prediction beyond exchangeability
Conformal prediction is a popular, modern technique for providing valid predictive inference for arbitrary machine learning models. Its validity relies on the assumptions of exchangeability of the data, and symmetry of the given model fitting algorithm as a function of the data. However, exchangeability is often violated when predictive models are deployed in practice. For example, if the data distribution drifts over time, then the data points are no longer exchangeable; moreover, in such settings, we might want to use an algorithm that treats recent observations as more relevant, which would violate the assumption that data points are treated symmetrically. This paper proposes new methodology to deal with both aspects: we use weighted quantiles to introduce robustness against distribution drift, and design a new technique to allow for algorithms that do not treat data points symmetrically. Our algorithms are provably robust, with substantially less loss of coverage when exchangeability is violated due to distribution drift or other challenging features of real data, while also achieving the same coverage guarantees as existing conformal prediction methods if the data points are in fact exchangeable. Finally, we demonstrate the practical utility of these new tools with simulations and real-data experiments.
- Slides (to come)
- Watch the replay (to come)
Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was a postdoc at UC Berkeley (2015–2018) and obtained his PhD at CMU (2010–2015), receiving the Umesh K. Gavaskar Memorial Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay (2005-09), and he did high-frequency algorithmic trading at a hedge fund (Tower Research) from 2009-10.
Aaditya was an inaugural inductee of the COPSS Leadership Academy, and a recipient of the 2021 Bernoulli New Researcher Award. His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award (2020), an ARL Grant on Safe Reinforcement Learning, the Block Center Grant for election auditing, a Google Research Scholar award (2022) for structured uncertainty quantification, amongst others.
Aaditya’s main theoretical and methodological research interests include selective and simultaneous inference (interactive, structured, online, post-hoc control of false decision rates, etc), game-theoretic statistics (sequential uncertainty quantification, confidence sequences, always-valid p-values, safe anytime-valid inference, e-processes, supermartingales, etc), and distribution-free black-box predictive inference (conformal prediction, calibration, etc). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, financial), and his group’s work has received multiple best paper awards.