Hey there! I’m a 4th year PhD student in the joint program between Statistics & Data Science and Machine Learning departments at Carnegie Mellon University. I’m fortunate to be advised by Professor Aaditya Ramdas. We consider the topics around building reliable ML systems, and in particular, distribution-free uncertainty quantification (conformal prediction, calibration) and adaptations to the presence of distribution shifts. Before joining CMU, I obtained BSc and MSc degrees at Moscow Institute of Physics and Technology and Skoltech.
PhD in Statistics and Machine Learning, in progress
Carnegie Mellon University
MSc in Applied Mathematics, 2018
Skolkovo Institute of Science and Technology, Moscow Institute of Physics and Technology
BSc in Applied Mathematics, 2016
Moscow Institute of Physics and Technology
When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain – but not all – distribution shifts could result in significant performance degradation. In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially, making interventions by a human expert (or model retraining) unnecessary. While several works have developed tests for distribution shifts, these typically either use non-sequential methods, or detect arbitrary shifts (benign or harmful), or both. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate. In this work, we design simple sequential tools for testing if the difference between source (training) and target (test) distributions leads to a significant drop in a risk function of interest, like accuracy or calibration. Recent advances in constructing time-uniform confidence sequences allow efficient aggregation of statistical evidence accumulated during the tracking process. The designed framework is applicable in settings where (some) true labels are revealed after the prediction is performed, or when batches of labels become available in a delayed fashion. We demonstrate the efficacy of the proposed framework through an extensive empirical study on a collection of simulated and real datasets.
Trustworthy deployment of ML models requires a proper measure of uncertainty, especially in safety-critical applications. We focus on uncertainty quantification (UQ) for classification problems via two avenues – prediction sets using conformal prediction and calibration of probabilistic predictors by post-hoc binning – since these possess distribution-free guarantees for i.i.d. data. Two common ways of generalizing beyond the i.i.d. setting include handling covariate and label shift. Within the context of distribution-free UQ, the former has already received attention, but not the latter. It is known that label shift hurts prediction, and we first argue that it also hurts UQ, by showing degradation in coverage and calibration. Piggybacking on recent progress in addressing label shift (for better prediction), we examine the right way to achieve UQ by reweighting the aforementioned conformal and calibration procedures whenever some unlabeled data from the target distribution is available. We examine these techniques theoretically in a distribution-free framework and demonstrate their excellent practical performance.
We study three notions of uncertainty quantification – calibration, confidence intervals and prediction sets – for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data. With a focus towards calibration, we establish a ‘tripod’ of theorems that connect these three notions for score-based classifiers. A direct implication is that distribution-free calibration is only possible, even asymptotically, using a scoring function whose level sets partition the feature space into at most countably many sets. Parametric calibration schemes such as variants of Platt scaling do not satisfy this requirement, while nonparametric schemes based on binning do. To close the loop, we derive distribution-free confidence intervals for binned probabilities for both fixed-width and uniform-mass binning. As a consequence of our ‘tripod’ theorems, these confidence intervals for binned probabilities lead to distribution-free calibration. We also derive extensions to settings with streaming data and covariate shift.