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.
Paper tackles graph matching with partially correct seeds, improving performance guarantees.
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this paper, we m…
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated …
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic blo…
PPM improves graph matching for correlated Gaussian Wigner models with high probability.
OmniMatch algorithm perfectly matches graphs without edge correlation.
Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences,…
Improved graph matching using covariates for network data integration.
The paper shows how shared random seeds can reduce variance in machine learning evaluations.
We study a well known noisy model of the graph isomorphism problem. In this model, the goal is to perfectly recover the vertex correspondence between two edge-correlated Erdős-Rényi random graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. For seeded problems, our result pr…
TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.
We propose a consistent polynomial-time method for the unseeded node matching problem for networks with smooth underlying structures. Despite widely conjectured by the research community that the structured graph matching problem to be significantly easier than its worst case counterpart, well-known to be NP-hard, the …
New method stabilizes machine learning predictions across random seeds.
Fairness audits fail under missing protected labels, especially at zero access.
Study finds many Lagrangian fillings for certain Legendrian links.
New method fills cluster seeds with exact Lagrangian structures.
Many methods for automated software test generation, including some that explicitly use machine learning (and some that use ML more broadly conceived) derive new tests from existing tests (often referred to as seeds). Often, the seed tests from which new tests are derived are manually constructed, or at least simpler t…
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning. Many stochastic objective functions implicitly require a random number seed as input. By explicitly reusing a seed a user can exploit common …
ThiopheneIV is a new solver for implied volatility with proven monotonicity.
In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-bas…
New method finds 198,846 toric-colorable seeds of Picard number 5.
Optimized biopharmaceutical seed train design reduces variability and saves time.
Study on how intraclass variability affects Temporal Ensembling accuracy.
PNN-smoothing improves -means clustering by merging subsets' clusterings.
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…
Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biologica…
Efficiently selects seed nodes to maximize content influence in unknown social networks.
EML-CD discovers causal mechanisms from neural networks in a structured way.
Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision-making framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting c…
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdős-Rényi graphs. However, very little is known about the existence of e…
We prove that for a generic -dimensional integrable rolling distribution of contact elements (excluding developable seed and isotropic developable leaves) isometric correspondence of leaves of a general nature (independent of the shape of the seed) requires the Bäcklund transformation.
Paper proves uniqueness of a complex construction.
Derivative-informed models improve financial surrogates for accurate hedging and risk management.
Discrete knot theory models use lattice-filtered graphs to detect merging knot components.
New method uses cluster shapes to improve track finding in particle collisions.
PolicySynth improves synthetic data alignment with real data for better campaign decisions.
New algorithm finds approximate stationary points in non-convex optimization.
A cluster variety of Fock and Goncharov is a scheme constructed from the data related to the cluster algebras of Fomin and Zelevinsky. A seed is a combinatorial data which can be encoded as an matrix with integer entries, or as a quiver in special cases, together with formal variables. A mutation is a c…
Regularization improves stability and consistency of sparse autoencoders.
Optimizer memory affects learning rate sensitivity in shuffle order, impacting fine-tuning noise.
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then…
The paper proves there are many Lagrangian fillings for Legendrian links of affine type.
Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA relies on quantum, rather than thermal, effects to explore complex search spaces. For many classes of problems, QA is known to offer computat…
The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.
Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors. …
The study finds many Lagrangian fillings for Legendrian links of specific types.
A fast regime-split Black-Scholes implied volatility solver
The paper defines and studies discrete p-density and compression-radius profiles of lattice knots.