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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.

168,695 papers · 148 categories

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4488132176 · May 202619922001200920172026
48 results for stability thresholds

Proves finitely generated associated graded rings for valuations on log Fano pairs.

problem Stability thresholds of log Fano pairs.
method Proves finite generation of associated graded rings for valuations.
result Log Fano pairs are uniformly K-stable if their stability threshold is less than a certain value.

Solves Tian's stabilization problem for toric Fano manifolds.

problem Tian's stabilization problem for equivariant global log canonical thresholds.
method Expressed complex singularity exponents in terms of support and gauge functions from convex geometry.
result First general result on Tian's problem.

Gradient descent near stability threshold exhibits sharpness oscillations.

problem Understanding sharpness behavior near stability threshold in non-Euclidean norms.
method Interpreted EoS through Directional Smoothness and generalized sharpness under arbitrary norms.
result Non-Euclidean GD with generalized sharpness shows sharpness oscillations near 2/η2/η.

Momentum affects optimization differently at small vs large batch sizes near instability.

problem Understanding how momentum impacts optimization near the edge of stability.
method Demonstrated through batch-size dependent behavior of SGD with momentum.
result Momentum operates in two distinct regimes: amplifying stochastic fluctuations at small batch sizes and stabilizing at large batch sizes.

In this paper, we prove the openness of K-semistability in families of log Fano pairs by showing that the stability threshold is a constructible function on the fibers. We also prove that any special test configuration arises from a log canonical place of a bounded complement and establish properties of any minimizer o…

2019-07-04abs ↗pdf ↗

Let X be a normal complex projective variety with at worst klt singularities, and L a big line bundle on X. We use valuations to study the log canonical threshold of L, as well as another invariant, the stability threshold. The latter generalizes a notion by Fujita and Odaka, and can be used to characterize when a Q-Fa…

2017-06-14abs ↗pdf ↗

Paper proves existence of unique constant scalar curvature Kähler metric under certain conditions.

problem Existence of constant scalar curvature Kähler metrics on polarized manifolds.
method Direct proof using microscopic stability thresholds and conditions on the limit.
result Existence of a unique constant scalar curvature Kähler metric under specific conditions.

The paper classifies and analyzes the stability of elastic curves with fixed endpoints.

problem Classification and stability of pinned elasticae.
method Critical points of the length-penalized elastic bending energy among planar curves with fixed endpoints.
result Explicit parametrization and classification of all critical points with a threshold parameter \(\hatλ \simeq 0.70107\).

Gradient descent near stability threshold shows sharpness oscillations.

problem Understanding sharpness and stability in non-Euclidean norms during gradient descent.
method Interpreted EoS through Directional Smoothness, defined generalized sharpness for arbitrary norms.
result Non-Euclidean GD exhibits sharpness oscillations around the stability threshold.

New stability criteria for Fano varieties using generalized b-divisors.

problem Characterizing uniform KK-stability in Fano varieties.
method Introducing a new function ildeδ ildeδ and formalism for KK-stability, proving stability conditions for Kähler-Einstein metrics.
result Existence of a unique Kähler-Einstein metric implies uniform D\mathbf{D}-log KK-stability when ildeδ(D)>1 ildeδ(\mathbf{D}) > 1.

High-dimensional models become unstable when sample size falls below a critical level, leading to a phase transition.

problem Instability in high-dimensional learning models when sample size is insufficient.
method Proved the necessity of a Fisher eigenvalue threshold for stability, introduced Fisher floor for verification.
result A sharp phase transition between reliable concentration and inevitable failure in high-dimensional learning.

Weight decay stabilizes training dynamics by slowing progressive sharpening.

problem Understanding how weight decay affects training stability in deep learning models.
method Analyzing weight decay effects at the Edge of Stability, developing a mathematical framework.
result Weight decay dampens oscillations and stabilizes sharpness in CNNs, causing a phase transition in MLPs.

The study bounds the stability of Gaussian mixtures under small perturbations.

problem Stability of Gaussian mixtures under small changes in distribution.
method Deriving an explicit bound on parameter stability of spherical Gaussian Mixture Models (sGMM) in a pre-defined model class.
result Upper bound on parameter distance of close sGMMs to the original sGMM, dependent only on the original model.

This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.

problem Analyzing quantiles of heavy-tailed distributions with estimated parameters.
method Introduces a Q-Q orthogonality formulation to separate projection-direction and quantile-threshold effects.
result Decomposes the difference between empirical and population quantiles into three terms.

Extends probabilistic approach for Kahler-Einstein metrics on Fano manifolds.

problem Constructing Kahler-Einstein metrics on log Fano manifolds with non-discrete automorphism groups.
method Introduces Gibbs polystability and uses moment map constraint to break symmetry.
result Gibbs polystability conjectured to be equivalent to existence of Kahler-Einstein metric.

The paper explores how dynamic preconditioning affects the CLT in online averaging.

problem When does dynamic preconditioning preserve the Polyak-Ruppert CLT?
method The authors decompose the averaged error and identify a stabilization-rate threshold for the CLT to hold.
result The CLT holds if the dynamic remainder vanishes in L2L^2 and the stabilization rate exceeds a threshold.

The minimizer of a volume function is unique for klt singularities.

problem Uniqueness of the minimizer of the normalized volume function for klt singularities.
method Defining stability thresholds for valuations and showing K-semistability.
result The minimizer of the normalized volume function for a klt singularity is unique up to rescaling.

LLMs fail to match statistical ground truth despite stable run-to-run performance.

problem LLMs lack validation against statistical ground truth in automated scientific workflows.
method Introduced a behavioral evaluation framework for LLMs, separating four decision-making dimensions.
result LLMs can exhibit near-perfect stability but diverge from statistical ground truth.

New approach finds analytic interpretation of algebraic invariants for balanced metrics.

problem Finding analytic interpretation of algebraic invariants for balanced metrics.
method Using log canonical thresholds and basis divisors, the approach involves quantized Ding functionals on Bergman spaces.
result Each δ_m is the coercivity threshold of a quantized Ding functional on the m-th Bergman space, characterizing the existence of balanced metrics.

This paper enhances stability selection by evaluating overall results robustness and identifying optimal regularization values.

problem Improving the robustness and reliability of high-dimensional variable selection.
method Developed a stability estimator to evaluate stability of stability selection results, calibrating key parameters.
result Identified optimal regularization value and improved stability of variable selection.

AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.

problem Online high-dimensional quantile regression with structural sparsity.
method Adaptive Iterative Hard Thresholding (AIHT) alternates stochastic updates with adaptive hard-thresholding steps.
result AIHT achieves logarithmic regret for the sliding-window objective in high-dimensional settings.

Gradient descent forces neural network eigenvalues to a specific threshold.

problem Understanding why gradient descent drives eigenvalues to a specific threshold.
method Introduced edge coupling, a functional on consecutive iterate pairs, to explain the trajectory towards the eigenvalue threshold.
result Gradient descent forces the Hessian eigenvalue to the threshold 2/η2/η from arbitrary initialization.

Sparse connectivity improves generalization in neural networks below the Edge of Stability.

problem Generalization guarantees for fully-connected networks fail at the Edge of Stability.
method Analyzed sparse connectivity's impact on generalization in two-layer ReLU networks.
result Sparse connectivity changes the effective constraint, leading to non-vacuous generalization bounds.

Studied how heavy-tailed behavior affects SGD's generalization in quadratic optimization.

problem Link between heavy-tailed behavior and generalization in SGD.
method Used heavy-tailed stochastic differential equation and proved stability bounds.
result Stability of SGD depends on the loss function's tail behavior.

New criterion for cylinder stability in curved spaces.

problem Stability of cylinders in curved spaces.
method Extending Plateau-Rayleigh criterion to curved spaces and proving existence of instability threshold.
result Existence of a positive number L0L_0 for cylinder instability in E(κ,τ)\mathbb{E}(κ,τ) spaces.

Study how firm liquidation regimes affect shareholder value and stability.

problem Balancing shareholder value and financial stability during firm liquidation.
method Modelled forced liquidation in reduced form, solved singular stochastic control problem.
result Combining distress regions below and above ruin threshold improves both shareholder value and firm survival.

Develops a minimax optimal estimator for system stability under distribution shift.

problem Ensuring system reliability under changes in the underlying environment.
method Minimax optimal estimation of stability defined in terms of acceptable performance degradation.
result Characterizes the minimax convergence rate and demonstrates practical utility.

We survey some recent topics on singularities, with a focus on their connection to the minimal model program. This includes the construction and properties of dual complexes, the proof of the ACC conjecture for log canonical thresholds and the recent progress on the `local stability theory' of an arbitrary Kawamata log…

2017-12-04abs ↗pdf ↗

A new method for disentangling action sequences improves model stability.

problem Challenges in unsupervised disentanglement learning due to incomplete theories and abstract notions.
method Introducing disentangling action sequences and a novel fractional variational autoencoder (FVAE) framework.
result FVAE improves the stability of disentanglement for action sequences.