Gabriel Stoltz — accepted AI-conference papers

2026

Adaptive Momentum and Nonlinear Damping for Neural Network Training

Aikaterini Karoni, Rajit Rajpal, Benedict Leimkuhler, Gabriel Stoltz

ICML 2026poster

Momentum Stochastic Gradient Descent (mSGD) relies on a fixed momentum coefficient shared across all parameters, failing to account for the heterogeneous structure of modern loss landscapes. In this work, we adopt a continuous-time formulation to introduce individual, adaptive momentum coefficients …

Cited by 0 Source ↗Scholar ↗

2020

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions

Loucas Pillaud-Vivien, Francis Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz

AISTATS 2020poster

Poincaré inequalities are ubiquitous in probability and analysis and have various applications in statistics (concentration of measure, rate of convergence of Markov chains). The Poincaré constant, for which the inequality is tight, is related to the typical convergence rate of diffusions to their e…

Cited by 22 Source ↗PDF ↗Scholar ↗