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Found 22 papers, 4 papers with code
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy.
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications.
Straggler-Resilient Personalized Federated Learning
Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions.
Self-Consistency of the Fokker-Planck Equation
In this paper, we exploit this concept to design a potential function of the hypothesis velocity fields, and prove that, if such a function diminishes to zero during the training procedure, the trajectory of the densities generated by the hypothesis velocity fields converges to the solution of the FPE in the Wasserstein-2 sense.
An Agnostic Approach to Federated Learning with Class Imbalance
Federated Learning (FL) has emerged as the tool of choice for training deep models over heterogeneous and decentralized datasets.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks.
Federated Functional Gradient Boosting
First, in the semi-heterogeneous setting, when the marginal distributions of the feature vectors on client machines are identical, we develop the federated functional gradient boosting (FFGB) method that provably converges to the global minimum.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Sinkhorn Natural Gradient for Generative Models
In this regard, we propose a novel Sinkhorn Natural Gradient (SiNG) algorithm which acts as a steepest descent method on the probability space endowed with the Sinkhorn divergence.
Sinkhorn Barycenter via Functional Gradient Descent
In this paper, we consider the problem of computing the barycenter of a set of probability distributions under the Sinkhorn divergence.
Safe Learning under Uncertain Objectives and Constraints
More precisely, by assuming that Reliable-FW has access to a (stochastic) gradient oracle of the objective function and a noisy feasibility oracle of the safety polytope, it finds an $\epsilon$-approximate first-order stationary point with the optimal ${\mathcal{O}}({1}/{\epsilon^2})$ gradient oracle complexity (resp.
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match
Concretely, for a monotone and continuous DR-submodular function, \SCGPP achieves a tight $[(1-1/e)\OPT -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients and $O(1/\epsilon)$ calls to the linear optimization oracle.
A Decentralized Proximal Point-type Method for Saddle Point Problems
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network.
Aggregated Gradient Langevin Dynamics
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems.
One Sample Stochastic Frank-Wolfe
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wide-spread use in many machine learning applications.
A Stochastic Trust Region Method for Non-convex Minimization
We target the problem of finding a local minimum in non-convex finite-sum minimization.
Stochastic Conditional Gradient++
It is known that this rate is optimal in terms of stochastic gradient evaluations.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Recently, the decentralized optimization problem is attracting growing attention.
Accelerated Variance Reduced Block Coordinate Descent
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space.
Riemannian Tensor Completion with Side Information
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems.
Kinetic Energy Plus Penalty Functions for Sparse Estimation
In this paper we propose and study a family of sparsity-inducing penalty functions.
