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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.

168,695 papers · 148 categories

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85169254338 · Jun 202019922001200920172026
48 results for log minimality

Study confirms boundedness of certain singularities in log Fano geometry.

problem Boundedness of log Fano cone singularities and minimal log discrepancies.
method Analyzing local volumes and minimal log discrepancies of Kollár components.
result Boundedness of K-semistable log Fano cone singularities confirmed in dimension three.

Characterizes toroidal and semi-toric compactifications as log minimal models and applies to weak K-moduli.

problem Characterizing and applying toroidal and semi-toric compactifications to weak K-moduli.
method Characterizes toroidal and semi-toric compactifications as log minimal models and applies to weak K-moduli.
result Different proof of a theorem of Alexeev-Engel on weak K-moduli compactifications.

Log minimality proven for weak K-moduli compactifications of Calabi-Yau varieties.

problem Constructing weak K-moduli compactifications of Calabi-Yau varieties.
method Revisiting classical problem, proving log minimality of normalizations under conditions.
result Log minimality of weak K-moduli compactifications under certain conditions.

Let δg,nδ_{g,n} be the minimal dilatation of pseudo-Anosovs defined on an orientable surface of genus gg with nn punctures. Tsai proved that for any fixed g2g \ge 2, the logarithm of the minimal dilatation logδg,n\log δ_{g,n} is on the order of lognn\frac{\log n}{n}. The main result of this paper is that if 2g+12g+1 is relativel…

2012-05-14abs ↗pdf ↗

We study surfaces in Euclidean space R3{\mathbb R}^3 that are minimal for a log-linear density φ(x,y,z)=αx+βy+γyφ(x,y,z)=αx+βy+γy, where α,β,γα,β,γ are real numbers not all zero. We prove that if a surface is φφ-minimal foliated by circles in parallel planes, then these planes are orthogonal to the vector (α,β,γ)(α,β,γ) and the surface must…

2014-10-09abs ↗pdf ↗

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 ↗

After establishing suitable notions of stability and Chern classes for singular pairs, we use Kähler-Einstein metrics with conical and cuspidal singularities to prove the slope semistability of orbifold tangent sheaves of minimal log-canonical pairs of log general type. We then proceed to prove the Miyaoka-Yau inequali…

2016-11-18abs ↗pdf ↗

SUMO provides unbiased log marginal likelihood estimation for latent variable models.

problem Biased estimates of log marginal likelihood in latent variable models.
method Randomized truncation of infinite series for unbiased estimation.
result Models trained with SUMO give better test-set likelihoods than standard methods.

New framework improves EM algorithm convergence under log-Sobolev inequality.

problem Improving convergence of the EM algorithm.
method Extending gradient flow techniques to EM algorithm, using free energy representation.
result Exponential convergence of EM algorithm under log-Sobolev inequality.

SVRN accelerates Newton methods by reducing variance and improving performance.

problem Improving the efficiency of Newton methods for large-scale optimization problems.
method Stochastic Variance-Reduced Newton (SVRN) algorithm that accelerates Subsampled Newton and Iterative Hessian Sketch algorithms.
result SVRN accelerates Newton methods by reducing the number of passes over the data, achieving a significant improvement in performance.

The paper improves sparse Gaussian processes by optimizing predictive loss.

problem Optimizing predictive loss in sparse Gaussian processes.
method Direct loss minimization (DLM) for log-loss and square loss, with product sampling (uPS) and biased Monte Carlo (bMC) for non-conjugate cases.
result DLM shows significant performance improvement in both log-loss and square loss cases.

A new method combines online and offline learning to tackle contextual bandits with missing action support.

problem Learning optimal policies with logged data when the logging policy has deficient support.
method Hybrid approach using online exploration to exploit supported actions and offline learning to avoid unnecessary explorations.
result Determines an optimal policy with theoretical guarantees using minimal online explorations.

We explain SSL objectives as log-likelihoods in a data curation model.

problem Lack of understanding of SSL objectives as log-likelihoods.
method Formulate SSL objectives as a log-likelihood in a generative model of data curation.
result SSL methods can be understood as lower-bounds on a principled log-likelihood.

Paper extends SMM to weakly convex and multi-convex surrogates for non-convex optimization.

problem Non-convex optimization with weakly convex or multi-convex surrogates.
method Stochastic majorization-minimization with proximal regularization or block-minimization.
result Convergence rates for empirical and expected losses under non-i.i.d. data.

New algorithms reduce matching market regret to log(T) with improved stability.

problem Minimizing regret in two-sided matching markets with bandit feedback.
method Phase-based algorithm with local arm deletion to improve stability.
result Achieves Θ(log(T)) regret for markets with uniqueness consistency.

A new VIS approach improves log-likelihood estimation in latent variable models.

problem Challenges in achieving high log-likelihood with VI for complex posterior distributions.
method Uses forward χ2χ^2 divergence to optimize proposal distribution for better log-likelihood estimation.
result Consistently outperforms state-of-the-art baselines in log-likelihood and parameter estimation.

Efficiently matches random graphs with inhomogeneous edge probabilities.

problem Matching latent vertex correspondence between two correlated random graphs with inhomogeneous edge probabilities.
method Inspired by Ding et al. (2021), an efficient matching algorithm is developed with conditions on minimal average degree and minimal correlation.
result An efficient matching algorithm is obtained as long as the minimal average degree is at least Ω(log2n)Ω(\log^{2} n) and the minimal correlation is at least 1O(log2n)1 - O(\log^{-2} n).

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 ↗

GN algorithm solves batched bandit for nondegenerate functions near-optimally.

problem Batched bandit learning for nondegenerate functions.
method Introduces Geometric Narrowing (GN) algorithm with a O~(A+dT)\widetilde{\mathcal{O}} ( A_{+}^d \sqrt{T} ) regret bound and O(loglogT)\mathcal{O} (\log \log T) batches.
result GN achieves near optimal regret with minimal number of batches.

Study proves uniqueness of Yang-Mills field tangent cones in arbitrary dimensions.

problem Proving uniqueness of Yang-Mills field tangent cones.
method Log-epiperimetric inequality, Luckhaus type lemma, and curvature concentration exclusion.
result Uniqueness of tangent cones for Yang-Mills fields in arbitrary dimensions.

New privacy mechanism reduces error in query results.

problem Achieving privacy while minimizing noise in query results.
method Extended sufficient and necessary condition for (ε,δ)(ε, δ)-differential privacy for symmetric and log-concave noise densities.
result Significantly lower mean squared errors than Laplace and Gaussian mechanisms.

We prove that the minimal diameter of a hyperbolic compact orientable surface of genus gg is asymptotic to logg\log g as gg \to \infty. The proof relies on a random construction, which we analyse using lattice point counting theory and the exploration of random trivalent graphs.

2019-09-26abs ↗pdf ↗

A new Bayesian filtering method speeds up stochastic Newton optimization.

problem Minimizing log-convex functions using stochastic methods.
method Contextualizes the problem as Bayesian inference, applying Bayesian filtering to update estimates.
result Establishes conditions for diminishing effect of older observations, akin to momentum.

A new estimator for evaluating policies in unknown environments.

problem Evaluating policies when both logging policy and value function are unknown.
method Doubly-Robust (DR) off-policy evaluation (OPE) estimator, DRUnknown, that estimates both the logging policy and value function.
result DRUnknown achieves the smallest asymptotic variance and is optimal when both models are correctly specified.

This work improves regret minimization for logistic bandits by reducing dependence on a large constant.

problem Minimizing regret in logistic bandits with reduced dependence on a large constant.
method Experimental design procedure and warmup sampling algorithm.
result Achieves a minimax regret of \(O(\sqrt{d \dotμT\log(|\mathcal{X}|)})\) in the fixed arm setting.

Let X be a complex projective variety and D a reduced divisor on X. Under a natural minimal condition on the singularities of the pair (X, D), which includes the case of smooth X with simple normal crossing D, we ask for geometric criteria guaranteeing various positivity conditions for the log-canonical divisor K_X+D. …

2012-07-31abs ↗pdf ↗

For all nn, we define the nn-dimensional critical catenoid MnM_n to be the unique rotationally symmetric, free boundary minimal hypersurface of non-trivial topology embedded in the closed unit ball in Rn+1\Bbb{R}^{n+1}. We show that the Morse index MI(n)\text{MI}(n) of MnM_n satisfies the following asymptotic estimate as …

2017-09-04abs ↗pdf ↗