2 citations · 9 across the 46 of their papers we have counts for
10 papers · 2 filters
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Bogdan Butyrin, Eric Moulines, Alexey Naumov +3
In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA…
Neural Optimal Transport Meets Multivariate Conformal Prediction
Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii +4
We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal…
Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev +4
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary…
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games
Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini +2
We introduce Mean-Field Trust Region Policy Optimization (MF-TRPO), a novel algorithm designed to compute approximate Nash equilibria for ergodic Mean-Field Games (MFG) in finite s…
Statistical inference for Linear Stochastic Approximation with Markovian Noise
Sergey Samsonov, Marina Sheshukova, Eric Moulines +1
In this paper we derive non-asymptotic Berry-Esseen bounds for Polyak-Ruppert averaged iterates of the Linear Stochastic Approximation (LSA) algorithm driven by the Markovian noise…
Proximal Point Nash Learning from Human Feedback
Daniil Tiapkin, Daniele Calandriello, Denis Belomestny +5
Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley--Terry model, which may not…
Adaptive Cumulative Mass Calibration with Conformal Prediction
Daniil Kazantsev, Eric Moulines, Maxim Panov +2
Reliable probability estimates by classifiers are essential in high-risk applications. In practice, however, predicted probabilities are often miscalibrated, and many existing post…
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
Paul Mangold, Alain Durmus, Aymeric Dieuleveut +1
This paper proposes a novel analysis for the Scaffold algorithm, a popular method for dealing with data heterogeneity in federated learning. While its convergence in deterministic…
Rectifying Conformity Scores for Better Conditional Coverage
Vincent Plassier, Alexander Fishkov, Victor Dheur +4
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score…
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Marina Sheshukova, Sergey Samsonov, Denis Belomestny +4
In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the Stochastic Gradient Descent (SGD) algor…