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.
The paper establishes sub-gradient estimates and entropy formulas for quaternionic contact geometry heat equations.
Optimal algorithms for online convex optimization with missing sub-gradient observations.
ICCNLS models complex relationships as convex and concave components.
In this work and the supporting Part II, we examine the performance of stochastic sub-gradient learning strategies under weaker conditions than usually considered in the literature. The new conditions are shown to be automatically satisfied by several important cases of interest including SVM, LASSO, and Total-Variatio…
GADGET SVM uses gossip-based distributed learning for scalable SVMs.
Sub-gradient method recovers low-rank matrices robustly from noisy measurements.
FPCA optimizes fairness in target vectors' span.
New algorithm for robust high-dimensional linear regression is both fast and statistically optimal.
In this paper, we derive a sub-gradient estimate for pseudoharmonic maps from noncompact complete Sasakian manifolds which satisfy CR sub-Laplace comparison property, to simply-connected Riemannian manifolds with nonpositive sectional curvature. As its application, we obtain some Liouville theorems for pseudoharmonic m…
A new algorithm improves both computational efficiency and statistical optimality for robust low-rank matrix and tensor estimation.
Deeper models have a more favorable optimization landscape, making them more robust to noise.
The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithms are used when the risk functions are non-smooth and involve non-differentiable components. They have been long recognized as being slow co…
We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to …
In this paper, we first obtain the sub-Laplacian comparison theorem in a complete noncompact pseudohermitian manifold of vanishing torsion (i.e. Sasakian manifold). Secondly, we derive the sub-gradient estimate for positive pseudoharmonic functions in a complete noncompact pseudohermitian manifold which satisfies the C…
New framework learns labels at both bag and graph levels.
Geodesic convexity generalizes the notion of (vector space) convexity to nonlinear metric spaces. But unlike convex optimization, geodesically convex (g-convex) optimization is much less developed. In this paper we contribute to the understanding of g-convex optimization by developing iteration complexity analysis for …
GeoAdaLer enhances geometric understanding of Adam for stochastic optimization.
This work analyzes and improves stochastic gradient methods for GAN training.
Reduces error bounds by incorporating known operations into deep nets.
Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature s…
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probabil…
A neural network learns a convex regularizer for better image reconstruction.
New SPS variant improves non-smooth optimization without small gradients.
Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have simple and efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and …
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences. Standard supervised learning algorithms assume accurate in…
Paper relaxes stability and generalization assumptions for SGD.
A new algorithm speeds up sparse-penalized quantile regression solving non-convex penalties.
In this paper we propose a randomized primal-dual proximal block coordinate updating framework for a general multi-block convex optimization model with coupled objective function and linear constraints. Assuming mere convexity, we establish its convergence rate in terms of the objective value and feasibility m…
Geometric step decay schedules improve stochastic algorithms' convergence on sharp nonconvex problems.
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regressio…
Overview of non-stochastic-gradient SA algorithms in signal processing and ML.
A new neural network model identifies hysteresis universally.
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.