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Found 23 papers, 9 papers with code
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems.
Solving stochastic weak Minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI).
Screening Rules and its Complexity for Active Set Identification
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning.
Random extrapolation for primal-dual coordinate descent
We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function.
Improved Optimistic Algorithms for Logistic Bandits
For logistic bandits, the frequentist regret guarantees of existing algorithms are $\tilde{\mathcal{O}}(\kappa \sqrt{T})$, where $\kappa$ is a problem-dependent constant.
Improving Evolutionary Strategies with Generative Neural Networks
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions.
Stochastic Frank-Wolfe for Composite Convex Minimization
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints.
Safe Grid Search with Optimal Complexity
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task.
Neural Generative Models for Global Optimization with Gradients
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones.
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
We propose a new randomized coordinate descent method for a convex optimization template with broad applications.
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Results on multimodal neuroimaging problems with M/EEG data are also reported.
Joint quantile regression in vector-valued RKHSs
Addressing the will to give a more complete picture than an average relationship provided by standard regression, a novel framework for estimating and predicting simultaneously several conditional quantiles is introduced.
GAP Safe Screening Rules for Sparse-Group Lasso
For statistical learning in high dimension, sparse regularizations have proven useful to boost both computational and statistical efficiency.
Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term.
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance.
GAP Safe Screening Rules for Sparse-Group-Lasso
We adapt to the case of Sparse-Group Lasso recent safe screening rules that discard early in the solver irrelevant features/groups.
GAP Safe screening rules for sparse multi-task and multi-class models
The GAP Safe rule can cope with any iterative solver and we illustrate its performance on coordinate descent for multi-task Lasso, binary and multinomial logistic regression, demonstrating significant speed ups on all tested datasets with respect to previous safe rules.
Mind the duality gap: safer rules for the Lasso
In this paper, we propose new versions of the so-called $\textit{safe rules}$ for the Lasso.
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual Newton Ascent (SDNA).
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses
We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions.
Accelerated, Parallel and Proximal Coordinate Descent
In the special case when the number of processors is equal to the number of coordinates, the method converges at the rate $2\bar{\omega}\bar{L} R^2/(k+1)^2 $, where $k$ is the iteration counter, $\bar{\omega}$ is an average degree of separability of the loss function, $\bar{L}$ is the average of Lipschitz constants associated with the coordinates and individual functions in the sum, and $R$ is the distance of the initial point from the minimizer.
Parallel coordinate descent for the Adaboost problem
We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent.
Smooth minimization of nonsmooth functions with parallel coordinate descent methods
We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions.
