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Found 15 papers, 2 papers with code
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.
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
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.
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector that is further used to derive the final clustering partition.
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.
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.
On the Proximal Gradient Algorithm with Alternated Inertia
In this paper, we investigate the attractive properties of the proximal gradient algorithm with inertia.
An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
We address the problem of multi-class classification in the case where the number of classes is very large.
