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arXiv research

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.

169,341 papers · 148 categories

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6401,2801,9202,560 · Jun 202019922001200920182026
48 results for gap and smoothing results

Graph pruning improves neural network performance by addressing squashing and smoothing issues.

problem Over-squashing and over-smoothing in Graph Neural Networks.
method Proposes edge deletions to simultaneously address over-squashing and over-smoothing, optimizing spectral gap.
result Edge deletions improve generalization and distinguishability of nodes of different classes.

Study of spectral gaps in non-smooth spaces with bounded Ricci curvature.

problem Analyzing spectral gaps in non-smooth metric measure spaces.
method Establishing a Polya-Szego type inequality and applying it to show spectral gaps for the p-Laplace operator.
result Sharp spectral gap results for the p-Laplace operator on various non-smooth spaces.

Paper establishes a universal growth rate for smooth surrogate losses in classification.

problem Analyzing growth rates of consistency bounds for various surrogate losses.
method Proves square-root growth rate for smooth margin-based losses; extends to multi-class classification.
result Demonstrates a universal square-root growth rate for smooth comp-sum and constrained losses.

Study of hyperbolic polyhedral surfaces with regular faces.

problem Understanding the properties of hyperbolic polyhedral surfaces with regular faces.
method Combinatorial and geometric analysis of hyperbolic polyhedral surfaces with regular faces.
result There is a gap between the areas of non-smooth hyperbolic polyhedral surfaces and smooth hyperbolic surfaces.

Unpublished results of S Straus and W Browder state that two notions of homotopy equivalence for manifolds with smooth group actions - isovariant and equivariant - often coincide under a condition called the Gap Hypothesis; the proofs use deep results in geometric topology. This paper analyzes the difference between th…

2009-04-03abs ↗pdf ↗

Paper analyzes complexity of solving nonconvex-strongly-concave problems.

problem Finding approximate stationary points of nonconvex-strongly-concave minimax problems.
method Introduces a generic acceleration scheme to solve crafted subproblems.
result Algorithm nearly matches lower complexity bounds in general setting.

The paper sets limits on the number of ends of certain geometric structures.

problem Limits on the number of ends of smooth metric measure spaces.
method Analyzes the Bakry-Émery Ricci tensor and function degeneration to set limits.
result Establishes gap theorems for ends of smooth metric measure spaces under specific conditions.

Derives bounds for deterministic predictors using smooth loss functions.

problem Generalizing probabilistic predictors to deterministic ones.
method Exploits smoothness properties of loss and predictor classes, controlling the Jensen gap class through Rademacher complexity.
result Derives bounds for deterministic predictors involving flatness quantities from Jacobians and Hessians.

We study λλ-hypersurfaces that are critical points of a Gaussian weighted area functional Σex24dA\int_Σ e^{-\frac{|x|^2}{4}}dA for compact variations that preserve weighted volume. First, we prove various gap and rigidity theorems for complete λλ-hypersurfaces in terms of the norm of the second fundamental form A|A|. Sec…

2014-05-19abs ↗pdf ↗

Counterexample shows state-constrained optimal control problems can have Young measure gaps.

problem Existence of Young measure gaps in state-constrained optimal control problems.
method Provided a counterexample for smooth controllable systems state-constrained to the unit ball.
result Gap occurs in a regular setting with non-convex Lagrangian density.

Deep neural networks perform poorly on smooth functions despite strong approximation theory.

problem The gap between deep learning theory and practical performance on smooth functions.
method Computational framework to study DNN performance, comparing against best-in-class methods.
result There is a crucial gap between DNN approximation theory and practical performance, but it can be closed.

In this paper, we extend the sharp lower bounds of spectal gap, due to Chen- Wang [10, 11], Bakry-Qian [6] and Andrews-Clutterbuck [5], from smooth Riemaniannian manifolds to general metric measure spaces with Riemannian curvature-dimension condition RCD*(K;N).

2015-03-01abs ↗pdf ↗

We prove the Fundamental Gap Conjecture, which states that the difference between the first two Dirichlet eigenvalues (the spectral gap) of a Schrödinger operator with convex potential and Dirichlet boundary data on a convex domain is bounded below by the spectral gap on an interval of the same diameter with zero poten…

2010-06-09abs ↗pdf ↗

In this note, we give a new and simple proof of a result in {\cite{DX1}} which states that any smooth complete self-shrinker in R3\mathbb{R}^3 with second fundamental form of constant length must be a generalized cylinder Sk×R2k\mathbb{S}^k \times \mathbb{R}^{2-k} for some k2k\leq2. Moreover, we prove a gap theorem for smo…

2014-05-16abs ↗pdf ↗

Improved inference-time alignment using Best-of-N and smoothing.

problem Reward overoptimization in Best-of-N (BoN) due to poor proxy reward models.
method Introduced Soft Best-of-N (SBoN) and analyzed its performance through KL divergence and regret analysis.
result Smoothing helps SBoN mitigate reward overoptimization, especially when proxy reward quality is low.

Paper studies a new curvature system and proves rigidity and gap theorems.

problem Extending CPE conjecture to manifolds with specific structures.
method Introduces (φCPE)(\varphi-\mathrm{CPE}) system and proves rigidity and gap theorems.
result Proves rigidity and gap theorems for (φCPE)(\varphi-\mathrm{CPE}) solutions.

Unified analysis of conjugate gradients and accelerated methods using duality gap.

problem Minimizing convex quadratic functions efficiently.
method Approximate Duality Gap Technique to unify conjugate gradients and accelerated methods.
result Unified and self-contained proof of conjugate gradients without relying on Chebyshev polynomials.

The paper extends Ricci flow conditions to less restrictive bounds.

problem Extending Ricci flow conditions to less restrictive negative bounds.
method Generalizing known Ricci flow invariant non-negative curvature conditions to negative bounds.
result Metrics with curvature operator eigenvalues greater than -1 can be evolved by Ricci flow for some uniform time.

In the first part we use Gromov's K--area to define the K--area homology which stabilizes into singular homology on the category of pairs of compact smooth manifolds. The second part treats the questions of certain curvature gaps. For instance, the LL^\infty --curvature gap of complex vector bundles on a compact manif…

2012-02-20abs ↗pdf ↗

The L2L^2-\partial\overline\partial-Lemma is extended to complete Kähler manifolds with a gap in the spectrum.

problem Extending the L2L^2-\partial\overline\partial-Lemma to non-compact Kähler manifolds.
method Proving the L2L^2-\partial\overline\partial-Lemma on complete Kähler manifolds with a gap in the spectrum.
result The L2L^2-\partial\overline\partial-Lemma is generalized to complete Kähler manifolds.

Researchers prove rigidity for spectral gap on special metric spaces.

problem Proving rigidity for spectral gap on RCD(K,)RCD(K,\infty)-spaces.
method Lift of eigenfunctions to Wasserstein space, theory of regular Lagrangian flows.
result Sharp spectral gap achieved only by splitting off a 1-dimensional Gaussian space.

Gluon optimizes LMO-based methods for large-scale tasks, improving performance and theory-practice gap.

problem LMO-based methods lack theoretical support for practical implementation and smoothness assumptions.
method Introduces Gluon, a new LMO-based method with refined smoothness model.
result Gluon's theoretical stepsizes match fine-tuned values, closing the theory-practice gap.

We present a primal-dual algorithmic framework to obtain approximate solutions to a prototypical constrained convex optimization problem, and rigorously characterize how common structural assumptions affect the numerical efficiency. Our main analysis technique provides a fresh perspective on Nesterov's excessive gap te…

2014-06-20abs ↗pdf ↗

Epoch-GDA achieves optimal convergence rate for SCSC min-max problems.

problem Solving stochastic min-max problems with strong convexity and strong concavity.
method Epoch-wise stochastic gradient descent ascent method (Epoch-GDA) without additional assumptions.
result Achieves the optimal rate of O(1/T)O(1/T) for the duality gap of general SCSC min-max problems.

Optimal distributed algorithms for convex optimization problems.

problem Distributed optimization in networks with communication constraints.
method Modeling network constraints as affine constraints, applying Nesterov's accelerated gradient descent to the dual problem.
result Achieves optimal convergence rates comparable to centralized optimization with additional spectral gap cost.

Our main result is that if a generic convex domain in Rn\R^n collapses to a domain in Rn1\R^{n-1}, then the difference between the first two Dirichlet eigenvalues of the Euclidean Laplacian, known as the fundamental gap, diverges. The boundary of the domain need not be smooth, merely Lipschitz continuous. To motivate th…

2008-10-27abs ↗pdf ↗

This note studies an issue relating to essential smoothness that can arise when the theory of large deviations is applied to a certain option pricing formula in the Heston model. The note identifies a gap, based on this issue, in the proof of Corollary 2.4 in \cite{FordeJacquier10} and describes how to circumvent it. T…

2011-07-25abs ↗pdf ↗

Improves neuroimaging model precision and speed using Nesterov's smoothing.

problem High-dimensional brain image analysis for clinical diagnosis with structured sparsity.
method Proposes CONESTA, a first-order continuation algorithm that automatically adjusts smoothing parameters.
result Significantly outperforms state-of-the-art solvers in convergence speed and precision.