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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.

168,657 papers · 148 categories

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89177266354 · Jun 202019922001200920172026
48 results for differential smoothness

The paper examines differential smoothness in specific algebra types.

problem Differential smoothness in 3D skew polynomial algebras and diffusion algebras.
method Analyzes the properties of 3D skew polynomial algebras and diffusion algebras.
result Provides insights into the differential smoothness of these algebra types.

We consider differentiable maps in the setting of Abstract Differential Geometry and we study the conditions that ensure the uniqueness of differentials in this setting. In particular, we prove that smooth maps between smooth manifolds admit a unique differential, coinciding with the usual one. Thus smooth manifolds fo…

2013-11-25abs ↗pdf ↗

We generalize stochastic smoothing for gradient estimation of non-differentiable functions.

problem Gradient estimation for non-differentiable functions.
method Developed a general framework for relaxation and gradient estimation of non-differentiable black-box functions using stochastic smoothing with reduced assumptions.
result Empirically validated the effectiveness of variance reduction strategies for various non-differentiable tasks.

The study examines differential smoothness in specific Artin-Schelter regular algebras of dimension 5.

problem Investigating the differential smoothness of Artin-Schelter regular algebras of dimension 5.
method Analyzing the relationship between the number of generators and Gelfand-Kirillov dimension to identify structural obstructions.
result Certain two- and four-generator AS-regular algebras of global dimension five fail to admit a differential calculus, while a five-generator graded Clifford algebra provides a positive example.

We consider spectral sequences in smooth generalized cohomology theories, including differential generalized cohomology theories. The main differential spectral sequences will be of the Atiyah-Hirzebruch (AHSS) type, where we provide a filtration by the Cech resolution of smooth manifolds. This allows for systematic st…

2016-05-11abs ↗pdf ↗

Develops differential K-theory for noncommutative algebras.

problem Creating a differential extension of algebraic K-theory for noncommutative algebras.
method Introduces secondary transgression forms and a differential refinement of the smooth Serre--Swan correspondence.
result Subsumes differential K-theory for smooth manifolds and fits into a noncommutative differential cohomology hexagon diagram.

This paper shows vector bundles and differential bundles are equivalent in smooth manifolds.

problem Characterizing vector bundles in smooth manifolds.
method Introducing differential bundles in a tangent category and proving equivalence with vector bundles in smooth manifolds.
result Differential bundles in smooth manifolds are equivalent to vector bundles.

We establish a relation between smooth 2-functors defined on the path 2-groupoid of a smooth manifold and differential forms on this manifold. This relation can be understood as a part of a dictionary between fundamental notions from category theory and differential geometry. We show that smooth 2-functors appear in se…

2008-02-05abs ↗pdf ↗

This work speeds up hyperparameter selection for non-smooth convex models using implicit differentiation.

problem Optimizing hyperparameters of non-smooth convex models.
method Implicit differentiation of proximal gradient and coordinate descent methods.
result Implicit differentiation can speed up hyperparameter optimization, especially for non-smooth problems.

We construct differential equivariant K-theory of representable smooth orbifolds as a ring valued functor with the usual properties of a differential extension of a cohomology theory. For proper submersions (with smooth fibres) we construct a push-forward map in differential equivariant K-theory. Finally, we construct …

2009-05-26abs ↗pdf ↗

Smooth bundles with rough data maintain Hodge kernel isomorphism.

problem Maintaining Hodge kernel isomorphism for smooth bundles with non-smooth geometric data.
method Analyzing nilpotent differential operators and Hodge-Dirac-type operators under perturbations of geometric data.
result Kernels of Hodge-Dirac operators remain isomorphic under uniform perturbations of geometric data.

The paper studies boundedness of pseudo-differential operators on smooth manifolds.

problem Boundedness of pseudo-differential operators in LpL^p-LqL^q spaces on smooth manifolds.
method Using global symbols and extending Hörmander's condition, the paper investigates LpL^p-boundedness, LL^\infty-BMOBMO estimates, and LpL^p-LqL^q boundedness for Fourier multipliers and pseudo-differential operators.
result The paper proves LpL^p-LqL^q boundedness for the range 1<p2q<1<p \leq 2 \leq q<\infty.

Study differential operators over maps and their applications in supermanifolds.

problem Understanding differential operators over smooth maps and their applications.
method Recall and study differential operators, formal \hbar-differential operators, pullbacks by thick morphisms, and quantization of symplectic micromorphisms.
result Developed constructions and examples of differential operators over maps.

Variant of previous work on smooth algebraic functions with compact and non-compact preimages.

problem Constructing smooth algebraic functions with specific preimage properties.
method Explicit construction of smooth real algebraic functions with controlled preimage compactness.
result New results in singularity theory and real algebraic geometry.

Smooth complex surfaces with triple intersections using differential geometry.

problem Smooth complex surfaces with trivial canonical bundle and triple intersections.
method Explicit construction of local smoothings and solutions to nonlinear elliptic PDEs.
result Existence of smoothings for dd-semistable SNC complex surfaces with trivial canonical bundle.

DPlis improves privacy in deep learning models by smoothing loss functions.

problem Privacy leakage in deep learning models trained on private data and low model performance.
method DPlis constructs a smooth loss function to favor noise-resilient models.
result DPlis effectively boosts model quality and training stability under privacy constraints.

A new fuzzy clustering method using hyperbolic smoothing for large datasets.

problem Building fuzzy clusters for large data sets efficiently.
method A novel smoothing numerical approach to relax the sum-of-squares criterion, converting the problem into a differentiable optimization problem.
result The method produces better fuzzy partitions compared to traditional fuzzy CC-means.

Enhances privacy in federated learning with Laplacian smoothing.

problem Protecting data privacy in federated learning while maintaining model accuracy.
method Laplacian smoothing for differentially private federated learning (DP-Fed-LS).
result Improves model accuracy with differential privacy guarantee and membership privacy.

New method generates private synthetic data with optimal utility for smooth queries.

problem Achieving strong utility guarantees for meaningful downstream analysis of sensitive datasets.
method Proposes a polynomial-time algorithm for generating (ε,δ)(\varepsilon,δ)-differentially private synthetic data with minimax optimal error rates for smooth queries.
result Achieves a minimax error rate of Ok,d(nmin{1,kd})O_{k,d}(n^{-\min \{1, \frac{k}{d}\}}) for kk-smooth queries, up to a log(n)\log(n) factor.

We give a new description of the ring structure on the differential characters of a smooth manifold via the smooth hyperspark complex. We show the explicit product formula, and as an application, calculate the product for differential characters of the unit circle. Applying the presentation of spark classes by smooth h…

2008-08-05abs ↗pdf ↗

New algorithm for differentially private distributed optimization of smooth, non-convex problems.

problem No differentially private distributed method for smooth, non-convex optimization problems.
method Smoothed normalization integrated with an error-feedback mechanism.
result Achieves superior convergence rate and first differentially private distributed optimization algorithm with provable convergence guarantees.

Paper introduces a differentially private generative model using gradient flow and sliced Wasserstein distance.

problem Protecting privacy in sensitive training data for generative models.
method Gradient flow in the space of probability measures, Gaussian-smoothed Sliced Wasserstein Distance, and numerical scheme for SDE.
result Demonstrates higher-fidelity data generation at low privacy budget compared to existing methods.

Introduces a new geometric framework for non-perturbative BV-theory.

problem Non-perturbative generalization of BV-theory in infinite-dimensional spaces.
method Derived differential geometry and homotopical algebraic geometry.
result Concrete model of derived smooth stacks for encoding non-perturbative BV-theory.

Paper proves Whitney stratified spaces can be given a conically smooth structure.

problem Proving Whitney stratified spaces can be given a conically smooth structure.
method Introduced conically smooth structure by Ayala, Francis, and Tanaka. Proved conjecture that any Whitney stratified space admits a canonical conically smooth structure.
result Established a connection between Whitney stratified spaces and conically smooth spaces.

Paper relaxes SGD privacy and generalization guarantees for non-smooth convex losses.

problem Privacy and generalization in SGD for non-smooth convex losses.
method Relaxes Lipschitz and strong smoothness assumptions to Hölder smoothness, proving (ε,δ)(ε,δ)-DP and optimal excess risk.
result Noisy SGD with αα-Hölder smooth losses achieves optimal excess risk with linear gradient complexity for α1/2α \geq 1/2.