A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
MARINA-P improves non-smooth federated optimization with adaptive stepsizes.
New method optimizes hyperparameters for non-smooth problems efficiently.
New algorithm controls linear systems with bandit feedback, achieving optimal regret.
We consider the problem of finding critical points of functions that are non-convex and non-smooth. Studying a fairly broad class of such problems, we analyze the behavior of three gradient-based methods (gradient descent, proximal update, and Frank-Wolfe update). For each of these methods, we establish rates of conver…
We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. regularization impedes the …
Extends curve theory to non-smooth data with finite curvature and torsion.
AsylADMM improves gossip-based learning for non-smooth objectives.
Survey on preserving curvature bounds for non-smooth Ricci flow.
We introduce non-smooth symplectic forms on manifolds and describe corresponding Poisson structures on the algebra of Colombeau generalized functions. This is achieved by establishing an extension of the classical map of smooth functions to Hamiltonian vector fields to the setting of non-smooth geometry. For mildly sin…
In recent literature, a general two step procedure has been formulated for solving the problem of phase retrieval. First, a spectral technique is used to obtain a constant-error initial estimate, following which, the estimate is refined to arbitrary precision by first-order optimization of a non-convex loss function. N…
The paper explores various stationarity concepts in non-smooth optimization.
We consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for first-order methods. We present the first known results for the non-smooth case, which requires different analysis and a different algorithm…
Smoothness analysis of adversarial training reveals constraints cause more non-smoothness.
The economic life of an asset is the optimum length of its usefulness, which is the moment that the asset's expenses are minimum. In this paper, the economic life of physical assets, such as industry machine and equipment, can be interpreted as the moment that the minimum is reached by its equivalent property cost func…
Sharp uncertainty principle for nodal sets in singular spaces.
In the framework of Lorentzian warped products, we study the Friedmann-Robertson-Walker cosmological model to investigate non-smooth curvatures associated with multiple discontinuities involved in the evolution of the universe. In particular we analyze non-smooth features of the spatially flat Friedmann-Robertson-Walke…
This work speeds up hyperparameter selection for non-smooth convex models using implicit differentiation.
New algorithms optimize non-smooth, non-convex objectives with improved complexity.
In spite of several notable efforts, explaining the generalization of deterministic non-smooth deep nets, e.g., ReLU-nets, has remained challenging. Existing approaches for deterministic non-smooth deep nets typically need to bound the Lipschitz constant of such deep nets but such bounds are quite large, may even incre…
In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The best known iteration compl…
Positive mass theorem for non-smooth metrics on flat manifolds with corners.
The purpose of this work is to develop and study a distributed strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution while the regularizers are assumed deterministic, but are not required…
We investigate a generalization of the so-called metric splitting of globally hyperbolic space-times to non-smooth Lorentzian manifolds and show the existence of this metric splitting for a class of wave-type space-times. Our approach is based on smooth approximations of non-smooth space-times by families (or sequences…
Advances smooth over-parameterization for solving non-smooth optimization problems.
New methods improve convergence in non-convex non-smooth learning problems.
Adaptive data fusion boosts efficiency in multi-task optimization.
Algebras of generalized functions offer possibilities beyond the purely distributional approach in modelling singular quantities in non-smooth differential geometry. This article presents an introductory survey of recent developments in this field and highlights some applications in mathematical physics.
New algorithm for robust high-dimensional linear regression is both fast and statistically optimal.
Abstracts a theorem for non-smooth maps in infinite dimensions.
New SPS variant improves non-smooth optimization without small gradients.
SAPPHIRE tackles ill-conditioned rERM problems with faster convergence.
Develops new synthetic Ricci flow concepts for metric measure spaces.
Stochastic approximation proves asymptotic normality for non-smooth problems.
Bayesian optimization tackles non-smooth tuning problems.
We provide improved convergence rates for various \emph{non-smooth} optimization problems via higher-order accelerated methods. In the case of regression, we achieves an iteration complexity, breaking the barrier so far present for previous methods. We arrive at a similar rate fo…
A new algorithm improves both computational efficiency and statistical optimality for robust low-rank matrix and tensor estimation.
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level. The canonical pivotal estimator is the square-root Lasso, formulated along with its derivatives as a "non-smooth + non-smooth" optimization problem. Modern technique…
RO-TD learns sparse value functions efficiently.
Safe-EF improves federated learning for non-smooth, constrained optimization.
We theoretically discuss why deep neural networks (DNNs) performs better than other models in some cases by investigating statistical properties of DNNs for non-smooth functions. While DNNs have empirically shown higher performance than other standard methods, understanding its mechanism is still a challenging problem.…
Modified perturbation method removes non-smoothness in solving Black-Scholes equations.
Recommendation is the task of improving customer experience through personalized recommendation based on users' past feedback. In this paper, we investigate the most common scenario: the user-item (U-I) matrix of implicit feedback. Even though many recommendation approaches are designed based on implicit feedback, they…
The study extends curvature bounds to non-smooth spaces and proves stability of mean curvature.
Expanding FCCO to non-smooth weakly-convex problems, improving deep learning performance.
Novel method for shape optimization of non-smooth PDEs.
Injectivity of X-ray transform proven for non-smooth metrics.
Timelike curvature and Brunn-Minkowski inequality linked in non-smooth spacetimes.
Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object…