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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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54108162216 · Jun 202019922001200920172026
48 results for ranking stability

Study higher rank deformed Hermitian-Yang-Mills equations for stable vector bundles.

problem Stability conditions for higher rank vector bundles over complex manifolds.
method Establish equivalence between dHYM equations and ZZ-stability.
result Equivalence between dHYM solutions and ZZ-stability for vortex type bundles.

The paper examines how gradient descent stabilizes low-rank matrix factorization in noisy conditions.

problem Stability of low-rank implicit regularization in perturbed deep matrix factorization.
method Derives spectral conditions for gradient descent to exhibit a low-rank phase in noiseless settings and analyzes perturbed dynamics.
result Gradient descent converges to a low-rank solution under perturbation, with explicit dependence on perturbation size.

Batch normalization prevents rank collapse in deep networks, improving training stability.

problem Rank collapse in randomly initialized deep networks with increasing depth.
method Investigates spectral instabilities in random matrices and uses batch normalization to avoid rank collapse.
result Batch normalization prevents rank collapse in both linear and ReLU networks, improving training stability.

We reveal a model rank that predicts successful recovery of target functions at overparameterization.

problem Understanding the mysterious good generalization performance of overparameterized nonlinear models.
method Rank stratification and linear stability theory for general nonlinear models.
result Linearly stable functions are preferred by nonlinear training, and model rank predicts minimal training data size.

Differentiable sorting and rank normalization are incompatible, with specific conditions for admissibility.

problem Incompatibility between differentiable sorting and rank normalization.
method Formalized admissibility through monotone invariance, batch independence, and rank-space stability conditions.
result Different gap-sensitive and batchwise relaxations of rank normalization violate the conditions for admissibility.

New model predicts stock performance in large equity markets.

problem Predicting stock performance in large equity markets over long time horizons.
method Rank-based volatility stabilized models calibrated to empirical data.
result The model exhibits relative arbitrage and statistically fits empirical features.

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.

The paper studies K-stability of spherical varieties and their degenerations.

problem Understanding K-stability and degenerations of polarized spherical varieties.
method Reduction to a variational problem on the moment polytope, convexity constraint, and solving the HMA equation.
result Determines strict semistability and polystable degenerations for Fano spherical varieties of rank two.

Proves stability of certain vector bundles on Kähler surfaces.

problem Stability of rank 2 holomorphic vector bundles on Kähler surfaces.
method Proves existence of ZZ-positive and ZZ-critical metrics leading to bundle stability.
result Proves stability results for deformed Hermitian Yang-Mills and almost Hermite-Einstein equations for rank 2 bundles.

New examples of deformed Hermitian-Yang-Mills connections found.

problem Constructing deformed Hermitian-Yang-Mills connections on manifolds.
method Constructed first higher rank, irreducible deformed Hermitian-Yang-Mills connections in both small and large radius regimes.
result Existence of solutions with any possible angle and ruling out some stability conditions.

Persistent homology analysis provides means to capture the connectivity structure of data sets in various dimensions. On the mathematical level, by defining a metric between the objects that persistence attaches to data sets, we can stabilize invariants characterizing these objects. We outline how so called contour fun…

2019-06-11abs ↗pdf ↗

Invariant kernels reduce rank and improve generalization across dimensions.

problem Symmetry in high-dimensional data impacts kernel matrix rank and learning algorithms.
method Compute invariant polynomial kernel ranks under various groups acting on data.
result Symmetry decreases kernel rank, making it independent of data dimension.

We introduce a new parameterization method for deep learning layers using spectral tensor train decomposition.

problem Efficiency and stability in deep learning models with weight matrix compression.
method Spectral Tensor Train Parameterization (STTP) of weight matrices.
result Improved compression and training stability in neural networks.

New methods ensure feature importance rankings are correct with high probability.

problem Stability issues in feature importance scores due to random sampling.
method Hypothesis testing-based techniques to assess and verify the stability of top-ranked features.
result Ensures the most important features are correct with high-probability guarantees.

Global stability bounds for matrix frames in phase retrieval problems.

problem Phase retrieval for matrix frames in various applications.
method Computable global stability bounds for the quasi-linear analysis map β, using Whitney stratification of positive semidefinite matrices of low rank.
result Novel conditions for a frame to be generalized phase retrievable.

PLUMAGE improves large model training efficiency and stability.

problem Accelerator memory and networking constraints during large model training.
method Probabilistic Low rank Unbiased Minimum Variance Gradient Estimator (PLUMAGE) that resolves bias and variance issues.
result PLUMAGE reduces training loss by 28% on average across the GLUE benchmark.

SL(3,Z) contains subgroups whose intersection is not finitely generated.

problem Identifying subgroups of SL(3,Z) whose intersection is not finitely generated.
method Explicit construction of subgroups H and K, using Schreier graph of an affine action of a free group on Z^2.
result Intersection of two 2-generated subgroups H and K in SL(3,Z) is not finitely generated.

New recommendations improve Gaussian process accuracy and stability.

problem Numerical instabilities and poor test likelihoods in iterative Gaussian process learning.
method Investigated CG tolerance, preconditioner rank, and Lanczos decomposition rank. Recommended small CG tolerance and large root decomposition size.
result L-BFGS-B optimizer achieves convergence with fewer gradient updates, improving Gaussian process accuracy.

We introduce the notions of categorical systoles and categorical volumes of Bridgeland stability conditions on triangulated categories. We prove that for any projective K3 surface, there exists a constant C depending only on the rank and discriminant of its Picard group, such that $$\mathrm{sys}(σ)^2\leq C\cdot\mathrm{…

2018-03-26abs ↗pdf ↗

We formulate a stability conjecture for the coefficients of the colored Jones polynomial of a knot, colored by irreducible representations in a fixed ray of a simple Lie algebra, and verify it for all torus knots and all simple Lie algebras of rank 22. Our conjecture is motivated by a structure theorem for the degree …

2013-10-26abs ↗pdf ↗

A new method for efficiently updating large-scale matrices in real-time.

problem Updating large-scale matrices with evolving data in real-time.
method Incremental SVD approach that handles row/column appends, rank-1 updates, and refresh strategies.
result Incremental SVD achieves accuracy close to full SVD with a fraction of the computational cost.

RoSHAP stabilizes feature attribution in machine learning models.

problem Stochastic variation in feature attribution measures.
method Modeling feature attribution score distribution and estimating it through bootstrap resampling and kernel density estimation.
result RoSHAP provides stable feature rankings and improves model performance.

A generalized Baumslag-Solitar (GBS) group is a finitely generated group acting on a tree with infinite cyclic edge and vertex stabilizers. We show how to determine effectively the rank (minimal cardinality of a generating set) of a GBS group; as a consequence, one can compute the rank of the mapping torus of a finite …

2013-04-29abs ↗pdf ↗

Geodesic spheres in certain symmetric spaces are quantitatively stable under small perturbations.

problem Stability of geodesic spheres in symmetric spaces under perturbations.
method Quantitative stability analysis using spectral gap of the Laplacian on geodesic spheres.
result Geodesic spheres are uniformly stable with respect to small C1C^1-volume preserving perturbations.

We introduce ZZ-critical connections for holomorphic vector bundles and prove their existence under stability conditions.

problem Existence of ZZ-critical connections for holomorphic vector bundles.
method Associated geometric PDEs to Bridgeland stability conditions and used infinite dimensional moment maps.
result In the large volume limit, a sufficiently smooth holomorphic vector bundle admits a ZZ-critical connection if and only if it is asymptotically ZZ-stable.

New findings on stability and Q-conditions for free group actions in hyperbolic spaces.

problem Understanding stability and Q-conditions for free group actions in hyperbolic spaces.
method Generalization of Minsky's and Bowditch's results to higher dimensions and W_3-extensible representations.
result Equivalence between primitive stability and generalized Q-conditions for F_2 in hyperbolic d-space (d >= 3).