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.
Boundary of fiber convex domains is a cohomological sphere.
Study shows how to control jump-diffusion processes with stable feedback controls in reinforcement learning.
New framework for RL with linear-convex models reduces performance gap.
We point out, and draw some consequences of, the fact that the Poisson Lie group G* dual to G=GL_n(C) (with its standard complex Poisson structure) may be identified with a certain moduli space of meromorphic connections on the unit disc having an irregular singularity at the origin. The Riemann-Hilbert map for such co…
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 …
A new algorithm finds optimal solutions for constrained decision processes.
Convex regression is a promising area for bridging statistical estimation and deterministic convex optimization. New piecewise linear convex regression methods are fast and scalable, but can have instability when used to approximate constraints or objective functions for optimization. Ensemble methods, like bagging, sm…
A lot of effort has been invested into characterizing the convergence rates of gradient based algorithms for non-linear convex optimization. Recently, motivated by large datasets and problems in machine learning, the interest has shifted towards distributed optimization. In this work we present a distributed algorithm …
FedCONST adapts update magnitudes to enhance feature generalization in FL.
New PL invariant classifies K3 surface degenerations.
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…
We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In this approach, one embeds each of the finitely many predictions (e.g.\ rankings) as a point in , assigns the original loss val…
Paper addresses ERM in LDP, reducing sample complexity for smooth and convex losses.
Adaptive SAA solves large-scale stochastic linear programs efficiently.
Improved MMWU algorithm achieves instance-optimal regret bound for matrix LEA.
Optimizes nonconvex optimization by converting it to static regret minimization.