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Found 18 papers, 5 papers with code
On the Iteration Complexity of Hypergradient Computations
We study a general class of bilevel optimization problems, in which the upper-level objective is defined via the solution of a fixed point equation.
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map.
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
In recent years, bilevel approaches have become very popular to efficiently estimate high-dimensional hyperparameters of machine learning models.
Variance reduction techniques for stochastic proximal point algorithms
Stochastic proximal point algorithms have been studied as an alternative to stochastic gradient algorithms since they are more stable with respect to the choice of the stepsize but a proper variance reduced version is missing.
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
In this work we study high probability bounds for stochastic subgradient methods under heavy tailed noise.
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start
We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map.
Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport
In this work we propose a batch version of the Greenkhorn algorithm for multimarginal regularized optimal transport problems.
The method of Bregman projections in deterministic and stochastic convex feasibility problems
We analyze in depth the case of affine feasibility problems showing that the iterates generated by the proposed methods converge Q-linearly and providing also explicit global and local rates of convergence.
Optimization and Control 90C25, 65K05, 49M37, 90C15, 90C06
Convergence Properties of Stochastic Hypergradients
Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning.
On the Iteration Complexity of Hypergradient Computation
We study a general class of bilevel problems, consisting in the minimization of an upper-level objective which depends on the solution to a parametric fixed-point equation.
Efficient Tensor Kernel methods for sparse regression
Second, we use a Nystrom-type subsampling approach, which allows for a training phase with a smaller number of data points, so to reduce the computational cost.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence.
Bilevel learning of the Group Lasso structure
Regression with group-sparsity penalty plays a central role in high-dimensional prediction problems.
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning.
Latent Variable Time-varying Network Inference
The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point.
Solving $\ell^p\!$-norm regularization with tensor kernels
In this paper, we discuss how a suitable family of tensor kernels can be used to efficiently solve nonparametric extensions of $\ell^p$ regularized learning methods.
Generalized support vector regression: duality and tensor-kernel representation
In this paper we study the variational problem associated to support vector regression in Banach function spaces.
