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.
SmartDCA improves investment returns by adjusting purchases based on prices.
New algorithms improve submodular minimization via DC programming.
The stochastic dual coordinate-ascent (S-DCA) technique is a useful alternative to the traditional stochastic gradient-descent algorithm for solving large-scale optimization problems due to its scalability to large data sets and strong theoretical guarantees. However, the available S-DCA formulation is limited to finit…
Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network rela…
DCA algorithm applied to SVR with RBF kernel for nonconvex optimization.
Few years ago we developed jointly with I.Dynnikov new discretization of complex analysis (DCA) based on the two-dimensional manifolds with colored black/white triangulation. Especially deep results were obtained for the Euclidean plane with equilateral triangle lattice. In the present work we develop a DCA theory for …
Comparison of decision curve analysis and cost curves for model evaluation.
A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem. The best-known solution to tackle general minimum conical hull problems is the divide-and-conquer anchoring learning scheme (DCA), whose runtime complexi…
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feasibility under a variety of operating conditions. In this paper, we explore an optimistic, or best-case view of uncertainty and show that it …
Paper solves high-order portfolio optimization with cardinality constraint.
We reduce a broad class of machine learning problems, usually addressed by EM or sampling, to the problem of finding the extremal rays spanning the conical hull of a data point set. These "anchors" lead to a global solution and a more interpretable model that can even outperform EM and sampling on generalizatio…
A new method for forming learning objectives using the sum of ranked range.
Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC appro…
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked go…
New method improves MAP inference for CGMs on path graphs, avoiding approximation and maintaining integrality.
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
Unified SVM algorithm for various losses with fast training.
Revisits PCA with new formulations and insights.
Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy. However, most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain c…
Paper tackles BNSL with IP, improving quality of solutions.
Paper proposes DC functions for better regularization of inverse problems with theoretical guarantees.
Introduces SoRR for aggregating losses in supervised learning.
We show how to compute the Bayes error-rate for speaker verifiers.
A new algorithm solves signed Fréchet regression on manifolds with bounded curvature.
Survey of spectral, probabilistic, and deep metric learning methods.
New method preserves privacy while improving machine learning accuracy.