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Found 15 papers, 4 papers with code
Universal Generalization Guarantees for Wasserstein Distributionally Robust Models
Distributionally robust optimization has emerged as an attractive way to train robust machine learning models, capturing data uncertainty and distribution shifts.
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
For generality, we focus on local solutions of constrained, non-monotone variational inequalities, and we show that the convergence rate of a given method depends sharply on its associated Legendre exponent, a notion that measures the growth rate of the underlying Bregman function (Euclidean, entropic, or other) near a solution.
Push--Pull with Device Sampling
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communication graph.
Federated Learning with Superquantile Aggregation for Heterogeneous Data
We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data.
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
In this paper, we analyze the local convergence rate of optimistic mirror descent methods in stochastic variational inequalities, a class of optimization problems with important applications to learning theory and machine learning.
Optimization in Open Networks via Dual Averaging
In networks of autonomous agents (e. g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest.
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities.
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications
Nonsmoothness is often a curse for optimization; but it is sometimes a blessing, in particular for applications in machine learning.
First-order Optimization for Superquantile-based Supervised Learning
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution.
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning.
Device Heterogeneity in Federated Learning: A Superquantile Approach
We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution.
On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems).
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
One of the main challenges is then to deal with heterogeneous machines and unreliable communications.
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm
We develop and analyze an asynchronous algorithm for distributed convex optimization when the objective writes a sum of smooth functions, local to each worker, and a non-smooth function.
Sensitivity Analysis for Mirror-Stratifiable Convex Functions
This pairing is crucial to track the strata that are identifiable by solutions of parametrized optimization problems or by iterates of optimization algorithms.
