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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.

168,742 papers · 148 categories

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8.3%16.7%25.0%33.3% · Jul 199219922001200920172026
48 results for star observation

Estimates how many times a star appears due to gravitational lensing.

problem Estimating the number of times an observer sees a star due to gravitational lensing.
method Use affine linking numbers to estimate the number of times an observer sees a star.
result Estimates the number of times an observer sees a star due to gravitational lensing.

New lower bound shows bandit convex optimization is harder than linear bandits.

problem Establishing a lower bound for stochastic bandit convex optimization.
method Constructing a hard class of convex functions and analyzing posterior spread of Fisher information matrices.
result A Ω~(d5/4T)\widetildeΩ(d^{5/4}\sqrt{T}) lower bound on minimax expected regret.

New lower bound shows bandit convex optimization is harder than previously thought.

problem Establishing a lower bound on the minimax expected regret for bandit convex optimization.
method Constructing a hard class of convex functions and analyzing the posterior spread of Fisher information matrices.
result A Ω~(d5/4T)\widetildeΩ(d^{5/4}\sqrt{T}) lower bound on the minimax expected regret.

We propose an explicit construction of the deformation quantization of the general second-class constrained system, which is covariant with respect to local coordinates on the phase space. The approach is based on constructing the effective first-class constraint (gauge) system equivalent to the original second-class o…

2001-01-14abs ↗pdf ↗

Study optimizes KSD estimation from samples, revealing Hilbert-Schmidt vs trace scales.

problem Optimizing estimation of Kernel Stein Discrepancy from samples.
method Identifying and comparing minimax scales for U-statistic and V-statistic.
result Hilbert-Schmidt norm of Stein covariance operator gives optimal scale.

STAR improves equivariant and invariant representation learning by routing projection heads.

problem Redundant feature learning in equivariant and invariant representation learning.
method Soft Task-Aware Routing (STAR) for projection heads specialization.
result Lower canonical correlations between invariant and equivariant embeddings.

Paper proves conjecture about star-shaped curves evolving under GAPF, but not always preserves star shape.

problem What conditions guarantee global existence of Gage's area-preserving flow for nonconvex initial curves?
method Using Dittberner's singularity analysis theory, constructed a ``flying wing'' curve to show limitations.
result Gage's area-preserving flow does not always preserve star-shapedness of evolving curves.

We analyze optimal weighted ridge regression in overparameterized linear models.

problem Optimal regularization in overparameterized linear regression models.
method Generalized ridge regression with weighted regularization.
result The optimal regularization parameter can be negative in overparameterized settings.

In this article, we introduce the notion of star-Ricci tensors in the real hypersurfaces of complex quadric QmQ^m. It is proved that there exist no Hopf hypersurfaces in Qm,m3Q^m,m\geq3, with commuting star-Ricci tensor or parallel star-Ricci tensor. As a generalization of star-Einstein metric, star-Ricci solitons on MM

2017-10-29abs ↗pdf ↗

Method identifies galaxies with recent star formation variations.

problem Identify galaxies with recent star formation variations.
method Approximate Bayesian Computation (ABC) with machine learning.
result Flexible star formation histories are needed for accurate modeling.

New algorithm for robust density estimation in corrupted data.

problem Density estimation in the presence of adversarial corruption.
method Proposes an algorithm for constructing a density estimator within a star-shaped density class, derived minimax bounds for estimation.
result Obtained minimax upper and lower bounds for density estimation under adversarial corruption.

New set-valued star-shaped risk measures introduced for better risk assessment.

problem Improving risk assessment in financial contexts.
method Developed new set-valued star-shaped risk measures and proved their representation theorems.
result Set-valued star-shaped risk measures can be represented as unions of set-valued convex risk measures.

The paper studies dynamic star-shaped risk measures and their representation.

problem Representing dynamic star-shaped risk measures and their properties.
method Representation theorems for dynamic monetary and star-shaped risk measures.
result Dynamic star-shaped risk measures can be represented as the lower envelope of a family of dynamic convex risk measures.

The paper characterizes dynamic return and star-shaped risk measures via BSDEs.

problem Characterizing dynamic return and star-shaped risk measures.
method Characterization of star-shaped functionals and BSDEs.
result Existence of convex BSDEs with non-empty set of supersolutions.

Deform moment map on symplectic connections using star product algebras.

problem Understanding symplectic connections and their deformations.
method Study vector bundle of Fedosov star product algebras, formal connection, curvature, and star product trace.
result Showed star product trace as a formal symplectic form and moment map.

Paper analyzes Birkhoff relaxation for graph alignment, providing theoretical guarantees.

problem Finding vertex correspondence between two graphs to maximize edge overlap.
method Birkhoff relaxation as a convex relaxation of the quadratic assignment problem (QAP).
result Theoretical guarantees on the performance of Birkhoff relaxation under specific conditions.

This paper optimizes Bayesian estimation for log-concave models using Langevin Monte-Carlo.

problem Optimizing Bayesian estimators for log-concave models with Langevin Monte-Carlo.
method Quantitative statistical bounds and numerical approximation of Gibbs measures.
result Established optimal numerical strategy and its cost for Bayesian posterior mean approximation.

New partition designs reduce star discrepancy in high-dimensional sampling.

problem Improving the expected star discrepancy in high-dimensional sampling.
method Developed non-equal volume partitions to achieve lower expected star discrepancy.
result Explicit upper bounds for expected star discrepancy under non-equal volume partitions.

We compare the star surgery operations introduced in [KS] to the generalized rational blow-down. We show that star surgery shares the properties that make rational blow-down useful for constructions of small exotic symplectic 4-manifolds. Then we show that star surgery operations provide a strictly more general class o…

2014-07-11abs ↗pdf ↗

We derive a closed formula for a star-product on complex projective space and on the domain SU(n+1)/S(U(1)×U(n))SU(n+1)/S(U(1)\times U(n)) using a completely elementary construction: Starting from the standard star-product of Wick type on Cn+1{0}C^{n+1} \setminus \{ 0 \} and performing a quantum analogue of Marsden-Weinstein reduction, we ca…

1995-03-09abs ↗pdf ↗

Improved SGD learning for single index models reduces sample complexity.

problem Learning a single index model with optimal sample complexity.
method Using smoothed loss in online SGD to reduce sample complexity.
result Online SGD with smoothed loss achieves optimal sample complexity of dk/2d^{k^\star/2}.

We analyze the structure of covariance matrices under graph constraints.

problem Analyzing the structure of covariance matrices under graph constraints.
method We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA) under a latent star topology.
result CMTFA can have either a rank 1 or a rank n-1 solution, with conditions for both.

New methods for private statistical inference under local differential privacy.

problem Private statistical inference for population means with bounded observations.
method Nonparametric, nonasymptotic statistical inference using a generalized randomized response mechanism.
result Private confidence intervals and sequences for population means under LDP constraints.

The paper finds at least four prime closed characteristics on star-shaped hypersurfaces in 8D space.

problem Finding prime closed characteristics on compact star-shaped hypersurfaces in 8D space.
method Proved existence of at least four prime closed characteristics for non-degenerate C3C^3 compact star-shaped hypersurfaces in R8\mathbb{R}^{8} without prime closed characteristics of Maslov-type index -1.
result Existence of at least four prime closed characteristics on compact star-shaped hypersurfaces in R8\mathbb{R}^{8}.

This paper connects monetary and star-shaped risk measures by showing their equivalence under certain conditions.

problem Understanding the relationship between monetary and star-shaped risk measures.
method Analyzing the acceptability of 0 and the normalization property.
result Monetary risk measures are only a translation away from star-shapedness under mild conditions.

The paper proves inequalities for star-shaped and FF-mean convex hypersurfaces in Rn+1\mathbb{R}^{n+1}.

problem Proving geometric inequalities for specific types of hypersurfaces.
method Using anisotropic pp-momentum, perimeter, and volume, the paper derives inequalities for star-shaped and FF-mean convex hypersurfaces.
result The Wulff shape of FF is the unique minimizer of the corresponding functionals among all star-shaped and FF-mean convex sets.

We define the notion of a formal connection for a smooth family of star products with fixed underlying symplectic structure. Such a formal connection allows one to relate star products at different points in the family. This generalizes the formal Hitchin connection introduced by the first author. We establish a necess…

2014-10-07abs ↗pdf ↗

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimu…

2019-01-02abs ↗pdf ↗

Constructs non-unital monoidal category of contact manifolds and Legendrian correspondence calculus

problem Constructing a non-unital monoidal category of contact manifolds without contact forms
method Developing contact topology without contact forms and defining the star product
result Proving the associativity of the star product and the pentagon axiom

The paper characterizes law-invariant star-shaped risk measures.

problem Understanding and characterizing law-invariant star-shaped risk measures.
method Developed characterizations for positively homogeneous and star-shaped functionals, derived Kusuoka-type representations, and offered representations of general law-invariant star-shaped functionals.
result Characterizations of law-invariant star-shaped functionals, including their connections to Value-at-Risk and Expected Shortfall.

This study uses deep learning to infer stellar parameters from short TESS and K2 observations.

problem Inferring precise stellar parameters from short-duration TESS and K2 observations.
method Developed a machine learning algorithm to infer asteroseismic parameters from one-month-long TESS observations of red giants.
result The algorithm can accurately infer ΔνΔν and νmaxν_{\mathrm{max}} for approximately 50% of TESS samples and ΔΠ1ΔΠ_{1} for about 200 young red-giants from K2.

Study tunnel numbers of cable knots and their companions, proving new bounds and constructing examples.

problem Understanding the relationship between the tunnel numbers of a knot and its cable.
method Combinatorial techniques and analysis of Heegaard splittings.
result Proves that for many cases, the tunnel number of a cable knot equals the original knot's tunnel number plus one.