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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.

169,051 papers · 148 categories

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56112167223 · Jun 202019922001200920182026
48 results for Mackey continuity

New map constructed from equivariant spectra for manifold study.

problem Understanding equivariant parametrized h-cobordism in non-manifold settings.
method Constructed a map from suspension G-spectrum to equivariant A-theory spectrum, compatible with tom Dieck splitting formulas.
result Fiber of constructed map is wedge of stable h-cobordism spectra.

We solve the regularity problem for Milnor's infinite dimensional Lie groups in the C0C^0-topological context, and provide necessary and sufficient regularity conditions for the (standard) CkC^k-topological setting. We prove that the evolution map is C0C^0-continuous on its domain iff\textit{iff}\hspace{1pt} the Lie gro…

2017-11-09abs ↗pdf ↗

We solve the regularity problem for Milnor's infinite dimensional Lie groups in the asymptotic estimate context. Specifically, let GG be a Lie group with asymptotic estimate Lie algebra g\mathfrak{g}, and denote its evolution map by evol ⁣:Ddom[evol]G\mathrm{evol}\colon \mathrm{D}\equiv \mathrm{dom}[\mathrm{evol}]\rightarrow G, i.e.…

2018-04-29abs ↗pdf ↗

In the dual LΦL_{Φ^*} of a Δ2Δ_2-Orlicz space LΦL_Φ, that we call a dual Orlicz space, we show that a proper (resp. finite) convex function is lower semicontinuous (resp. continuous) for the Mackey topology τ(LΦ,LΦ)τ(L_{Φ^*},L_Φ) if and only if on each order interval [ζ,ζ]={ξ:ζξζ}[-ζ,ζ]=\{ξ: -ζ\leq ξ\leqζ\} (ζLΦζ\in L_{Φ^*}), it is lowe…

2016-11-18abs ↗pdf ↗

RPNN-EOFs model improves time series forecasting accuracy.

problem Improving time series forecasting accuracy for complex systems.
method Combines higher-order neural networks with error-output feedbacks.
result RPNN-EOFs outperformed other models in forecasting the Mackey-Glass time series.

Mackey showed that for a compact Lie group KK, the pair (K,C0(K))(K,C^{0}(K)) has a unique non-trivial irreducible covariant pair of representations. We study the relevance of this result to the unitary equivalence of quantizations for an infinite-dimensional family of K×KK\times K invariant polarizations on TKT^{\ast}K. The …

2012-11-09abs ↗pdf ↗

Generalizes CNNs on homogeneous spaces like Euclidean and spherical surfaces.

problem Classifying and understanding equivariant CNNs on homogeneous spaces.
method Develops a theory for equivariant maps between field spaces of given types.
result Equivariant kernels correspond to the most general kind of equivariant linear maps.

Counterexample disproves completeness of model space forcing regularity in infinite-dimensional Lie groups.

problem Whether every Lie group modeled on a complete locally convex space is regular.
method Constructing a specific contractible complex analytic BCH-Lie group with unique properties.
result The group is not even C0C^0-semiregular, and smooth controls have no C1C^1 evolution.

Characterizes continuity of monotone functionals in mixed topology.

problem Continuity of monotone functionals in mixed topology.
method Characterization through lower semicontinuity and dual representations.
result Continuity in mixed topology is equivalent to dual representation in terms of countably additive measures.

Study on diffeologies on locally convex spaces and smooth multiplication of distributions.

problem Geometric characterization and smoothness of distribution multiplication.
method Investigation of canonical and cc^\infty-diffeologies on locally convex spaces, proving geometric characterizations, and comparing diffeologies.
result Established a framework for nonlinear distribution theory beyond manifolds, realizing microlocally multipliable distributions as a diffeological colimit.

Functor connects Lie groupoid algebras to bornological structures.

problem Establishing a functorial relationship between Lie groupoid convolution algebras and bornological structures.
method Developed a monoidal functor from differentiable stacks to Morita 2-category of complete bornological algebras.
result Convolution algebras are self-induced and convolution modules are smooth.

We construct for an equivariant cohomology theory for proper equivariant CW-complexes an equivariant Chern character, provided that certain conditions about the coefficients are satisfied. These conditions are fulfilled if the coefficients of the equivariant cohomology theory possess a Mackey structure. Such a structur…

2004-01-06abs ↗pdf ↗

Improved convergence rates for Stein Variational Gradient Descent in finite-particle settings.

problem Improving convergence rates for Stein Variational Gradient Descent in finite-particle settings.
method Analyzing the time derivative of relative entropy and splitting it into dominant and smaller parts.
result Finite-particle convergence rates of order 1/\sqrt{N} for Kernelized Stein Discrepancy and Wasserstein-2 metrics.

Combines Fourier methods and RNNs for efficient time series prediction.

problem Efficiently processing and predicting time series data with memory and computational constraints.
method Uses short-time Fourier transform and weight reductions through low pass filtering in a Spectral RNN.
result Predicts time series data from chaotic systems and real-world data.

We study diffeologies on locally convex spaces and their application to smooth multiplication of distributions.

problem Constructing smooth multiplication of distributions on locally convex spaces.
method Using diffeological colimits and wavefront-set criterion.
result Proving smooth multiplication of microlocally multipliable distributions.

ICA accurately estimates treatment effects even with confounders.

problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).

ForGAN uses GANs for probabilistic forecasting of sensory data.

problem Challenges in traditional forecasting methods and difficulties in probabilistic methods.
method ForGAN combines GANs with conditional generative adversarial networks to learn data distributions and generate probabilistic forecasts.
result ForGAN outperforms traditional regression methods in probabilistic forecasting of sensory data.

Extends particle classification to curved space-times using groupoids.

problem Classifying elementary particles in curved space-time.
method Developed a new definition of elementary particles as irreducible projective representations of kinematical groupoids, extending Wigner's program.
result Classification of elementary particles valid for a wide range of space-times, including new massless particles in magnetic-like backgrounds.

Compress++ speeds up distribution compression to near-linear time.

problem Accurately summarize a probability distribution using a small number of points efficiently.
method Introduces Compress++, a meta-procedure to speed up any thinning algorithm.
result Achieves n\sqrt{n} points with O(logn/n)\mathcal{O}(\sqrt{\log n/n}) integration error in O(nlog3n)\mathcal{O}(n \log^3 n) time and O(nlog2n)\mathcal{O}( \sqrt{n} \log^2 n ) space.

Global models outperform univariate benchmarks in complex time series forecasting.

problem Comparing global forecasting models to univariate benchmarks in various challenging scenarios.
method Simulated datasets with controlled characteristics, including homogeneity, complexity, and series lengths. Global forecasting models (RNN, LGBM) compared to univariate techniques.
result Global models like RNN and LGBM are competitive in complex scenarios with short series lengths and heterogeneous data.

Enhanced fuzzy system predicts chaotic time series with improved accuracy.

problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.

The paper studies continuous submodular functions and their optimization.

problem Maximizing continuous submodular functions in poly. time.
method Characterization of continuous submodularity, operations preserving it, and algorithms for constrained maximization.
result Continuous submodularity is equivalent to a weak DR property, leading to continuous DR-submodular functions with the full DR property.

Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.

problem Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.
method Normalized Ricci flow on compact surfaces.
result Uniform Lipschitz continuity of isoperimetric profiles under normalized Ricci flow.

Continuized Nesterov acceleration accelerates stochastic gradient descent and gossip algorithms.

problem Improving the convergence rate of stochastic gradient descent and gossip algorithms.
method Introducing a continuized variant of Nesterov acceleration, which mixes variables continuously and takes gradient steps at random times.
result The continuized Nesterov acceleration achieves convergence rates similar to Nesterov's original acceleration but with random parameters.

CANDI solves the gap between continuous and discrete diffusion models for text generation.

problem Underperformance of continuous diffusion models in discrete data domains.
method Introduces token identifiability and a hybrid framework (CANDI) to decouple discrete and continuous corruption.
result CANDI successfully avoids temporal dissonance, enabling continuous diffusion benefits for discrete spaces.

Study on Hölder continuity of complex Monge-Ampère solutions on Stein spaces.

problem Understanding continuity of solutions to complex Monge-Ampère equations on Stein spaces.
method Analyzing solutions with LpL^p densities and Hölder boundary data on Stein spaces with isolated singularities.
result Solutions are Hölder continuous outside singular points if boundary data is Hölder continuous.

Classifies when homeomorphism groups of stable surfaces have automatic continuity.

problem Determining when homeomorphism groups of stable surfaces are continuous.
method Developed a general framework to prove automatic continuity for homeomorphism groups, applied to stable surfaces and Stone spaces.
result Classification of stable surfaces with respect to automatic continuity of their homeomorphism groups.

Study on existence and properties of continuous solutions to complex Hessian equations.

problem Existence and properties of continuous solutions to complex Hessian equations.
method Established new capacity estimates and weak stability estimates for the mm-Hessian measure.
result Existence of continuous solutions to the complex Hessian equation under certain conditions.

Continuous time framework for discrete data denoising models.

problem Efficient training and sampling for discrete data denoising models.
method Formulated as Continuous Time Markov Chains (CTMCs), efficient training using continuous time ELBO, high-dimensional CTMC simulation, novel theoretical error bound.
result Continuous time treatment enables novel theoretical error bound between generated and true data distributions.

Study on continual learning with Twitter data, developing ConGraD algorithm.

problem Personalized online language learning on a massive scale.
method Developed POLL problem setting, collected Firehose datasets, and introduced ConGraD algorithm.
result ConGraD algorithm outperforms prior continual learning methods on Firehose datasets.