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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.

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48 results for rank-one modules

Study TMF-valued TQFT for closed 3-manifolds using torsion linking pairings.

problem Explicit description of TMF-module state space for closed 3-manifolds.
method Construct canonical invariants and tokenization from torsion linking pairings.
result Explicit model for TMF-module state space in terms of rank-one TMF-module.

In this paper, we consider the similarity and quasi-affinity problems for Hilbert modules in the Cowen-Douglas class associated with the complex geometric objects, the hermitian anti-holomorphic vector bundles and curvatures. Given a "simple" rank one Cowen-Douglas Hilbert module M\mathcal{M}, we find necessary and su…

2012-12-12abs ↗pdf ↗

We propose a new method for studying nn- and ΓΓ-cohomology of globalizations of Harish-Chandra modules, where G=KANG=KAN is a rank one semisimple Lie group, ΓΓ is a discrete subgroup of GG and n=Lie(N)n=Lie(N). We prove a conjecture of Patterson relating the singularities of Selberg zeta functions with the ΓΓ-cohomology of…

1994-11-18abs ↗pdf ↗

Study deformations of compact Calabi-Yau conifolds with singularities.

problem Understanding deformations of compact Calabi-Yau conifolds with singularities.
method Analyzes the obstructions and local triviality of deformations under specific topological and geometric hypotheses.
result Obstruction to deformations concentrates at singularities, generalizing previous results.

The paper reveals low-rank structure in neural network gradients, influenced by data and model parameters.

problem Investigating low-rank structure in gradients of neural networks under relaxed assumptions.
method Spiked data model, relaxation of isotropy assumptions, analysis of mean-field and neural-tangent-kernel scalings.
result Gradient of input weights is approximately low rank, dominated by two rank-one terms.

We develop a notion of rank one properly convex domains (or Hilbert geometries) in the real projective space. This is in the spirit of rank one non-positively curved Riemannian manifolds and CAT(0) spaces. We define rank one isometries for Hilbert geometries and characterize them as being equivalent to contracting elem…

2019-12-30abs ↗pdf ↗

The study establishes uncertainty principles on harmonic manifolds of rank one.

problem Developing uncertainty principles for harmonic manifolds of rank one.
method Derivation of various uncertainty principles including Heisenberg, Morgen, Schrödinger, and Hömanders principles.
result Generalization of Hausdorff-Young inequality to harmonic manifolds of rank one.

Improved stability for matrix recovery from rank-one measurements.

problem Phase retrieval problem of recovering rank-one positive semidefinite matrices.
method Developed a smoothing Newton method based on Bures-Wasserstein gradient descent.
result Superlinear convergence with rigorous guarantees and stable implementation.

Compact rank one symmetric spaces are rigid under certain curvature conditions.

problem Rigidity of compact rank one symmetric spaces under curvature constraints.
method Examined compact symmetric spaces with metric g0g_0 of rank one, and another metric gg with sectional curvature bounded by 0 to 1.
result If gg equals g0g_0 outside a convex subset, then gg is isometric with g0g_0.

Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are proved to have logarithmic regret, but do not match the existing lower bound for this problem. We close this gap by first proving that rank…

2019-12-06abs ↗pdf ↗

Novel algorithm for Markov decision processes using rank-one approximation.

problem Solving planning and learning problems of Markov decision processes.
method Policy iteration with rank-one approximation of transition probability matrix.
result The proposed algorithm consistently outperforms first-order algorithms and their accelerated versions.

In this paper, based on research on rank-one isometries by W.Ballmann and M.Brin and recent research on rank-one isometries of Coxeter groups by P.Caprace and K.Fujiwara, we study a topological fractal structure of boundaries of Coxeter groups. We also show that the limit-point set is dense in a boundary of a Coxeter g…

2009-12-01abs ↗pdf ↗

Study shows generative priors improve rank-one matrix recovery with optimal sample complexity.

problem Recovering a rank-one signal matrix from noisy data with additional prior information.
method Analysis of a nonlinear least squares objective with a favorable global optimization landscape.
result Established optimal sample complexity for generative priors in rank-one matrix recovery.

The paper analyzes tensor recovery from symmetric rank-one measurements using information theory.

problem Recovering tensors with low symmetric rank from symmetric rank-one measurements.
method Covering numbers argument, Carbery-Wright inequality, orthogonal polynomials, Fano's inequality.
result Near-optimal sample complexity bounds for log-concave distributions.

Gradient descent solves rank-one matrix estimation problem with detailed time evolution analysis.

problem Estimating a rank-one symmetric matrix corrupted by noise.
method Gradient descent on a sphere, using local versions of the semi-circle law.
result Explicit formulas for the time evolution of the estimator and cost function, revealing phase transitions.

We give examples of rank one compact surfaces on which there exist recurrent geodesics that cannot be shadowed by periodic geodesics. We build rank one compact surfaces such that ergodic measures on the unit tangent bundle of the surface are not dense in the set of probability measures invariant by the geodesic flow. F…

2010-04-29abs ↗pdf ↗

Let XX be a Hadamard manifold, and ΓΓ a non-elementary discrete group of isometries of XX which contains a rank one isometry. We relate the ergodic theory of the geodesic flow of the quotient orbifold M=X/ΓM=X/Γ to the behavior of the Poincar{é} series of ΓΓ. Precisely, the aim of this paper is to extend the so-called…

2015-08-24abs ↗pdf ↗

This work presents a novel approach to train invertible linear layers by adding rank-one perturbations.

problem Training invertible linear layers during optimization with gradient-based methods is challenging.
method Train rank-one perturbations and add them to weight matrices infrequently, keeping track of inverses and determinants.
result Invertible linear layers improve mixing and mode separation in normalizing flows.

There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby alleviating the overfitting and curse of dimensionality problems in the conventio…

2018-04-17abs ↗pdf ↗