Proposes an EM method for learning from positive and unlabeled data with random selection assumption.
problem Learning from positive and unlabeled data with random selection assumption.
method Proposes an EM method to learn under the assumption that positive examples are selected at random, conditioned on some attributes.
result The proposed method outperforms state-of-the-art methods for learning under the selected completely at random assumption.
Random forests decode index finger positions from EEG.
problem Decoding detailed body part positions from non-invasive EEG.
method Leave-one-subject-out cross-validation with random forest.
result Index finger positions can be distinguished with high accuracy.
Paper explores Polya's characterization of positive-definite kernels and random feature maps.
problem Characterizing positive-definite kernels and their random feature maps.
method Study Polya's criterion and derive novel kernels; compare random Fourier and binning feature maps.
result Random binning feature map yields a closer Euclidean inner product to the kernel.
New DKPP family controls positive and negative dependence in random subsets.
problem Challenges in seamlessly bridging probabilistic models for positive and negative dependence.
method Introduced DKPP family and developed computational methods for probabilistic operations and inference.
result Controllability of positive and negative dependence demonstrated through numerical experiments.
The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.
problem Predicting responses on out-of-sample nodes with latent positions on unknown curves.
method Manifold learning and graph embedding technique using latent positions.
result Convergence guarantees for predicting responses on out-of-sample nodes.
Two types of nonidentifiability in latent position graphs identified and characterized.
problem Identifying and characterizing nonidentifiability in latent position random graph models.
method Defined and examined subspace nonidentifiability and model-based nonidentifiability, providing examples and characterizing limits.
result Characterized the limits of model-based nonidentifiability and obtained additional limiting results for specific graph models.
IDPGs extend RDPGs with a Poisson process for random latent positions.
problem Modeling randomness in latent positions for graph structure.
method Introduce IDPGs using Poisson point processes on latent Euclidean space.
result Continuous analogues of adjacency matrices link latent structure to observed graphs.
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown. In particular, we follow the methodology outlined in \citet{sussman2012universally} to construct consistent estimates for the …
The study of random positive 3-strand braids reveals patterns in the roots of their Alexander polynomials.
problem Investigating the roots of Alexander polynomials of random positive 3-strand braids.
method Experimental data analysis, conjectures refinement, and proof of results using tools like the signature function of links and Lyapunov exponent of the Burau representation.
result Generically, at least 69% of the roots of Alexander polynomials are on the unit circle, with a large root-free region near the origin.
Random links defined by bridge splitting are hyperbolic with high probability.
problem Understanding the hyperbolic nature of random links.
method Random bridge splitting to define links and probabilistic analysis.
result Random links are hyperbolic with asymptotic probability 1.
Randomized positional encodings boost transformer performance on longer sequences.
problem Transformers struggle with generalizing to sequences of arbitrary length.
method Introduced randomized positional encodings that simulate longer sequences and randomly select positions.
result Randomized positional encodings increase test accuracy by 12.0% on average for sequences of unseen length.
Study shows mass distribution of random holomorphic sections follows a central limit theorem.
problem Understanding mass distribution of random holomorphic sections.
method Proved a central limit theorem for mass distribution of random holomorphic sections associated with positive line bundles.
result Almost every sequence of random holomorphic sections exhibits quantum ergodicity.
Study improves variance calculation for random zero sets on complex manifolds.
problem Improving the variance calculation for random zero sets on complex manifolds.
method Deriving an asymptotic expansion for the variance of linear statistics of zero divisors of random holomorphic sections.
result Sharpens leading-order asymptotics for the variance of random zero sets.
The Dirichlet random walk on manifolds has a positive escape rate if the cover is non-amenable.
problem Analyzing the stochastic behavior of Dirichlet random walks on manifolds.
method Defining a recursive process on Galoisian covers and proving a theorem about the escape rate.
result The escape rate is positive if and only if the cover is non-amenable.
Random 3-manifolds have exponential growth of torsion in covers.
problem Exponential growth of torsion in random 3-manifolds.
method Study of random 3-manifolds with positive first Betti number.
result Exponential growth of torsion in cyclic covers.
Neural networks learn patterns in random data, improving downstream performance.
problem Understanding what deep networks learn with random labels.
method Analytical and empirical study of convolutional and fully connected networks pre-trained on random labels.
result Pre-trained networks on random labels transfer faster to real datasets, despite specialization effects.
Fold maps associated to geodesic random walks on curved spaces.
problem Understanding the behavior of geodesic random walks on curved surfaces.
method Analyzing mappings from the unit tangent sphere to a manifold with non-positive curvature.
result For odd powers of the unit tangent sphere, these mappings are fold maps.
This paper explores GNN functions on random graphs, highlighting the importance of node Positional Encodings.
problem Understanding the expressive power of GNNs on large random graphs.
method General convergence notions, input node features, and Positional Encodings (PEs).
result GNNs can converge to certain functions on large random graphs, emphasizing the role of PEs.
Paper generalizes spectral embedding for better graph interpretation.
problem Modeling heterophilic connectivity and negative eigenvalues in graph data.
method Generalized latent position network model (Random Dot Product Graph).
result Consistent latent position estimates with asymptotically Gaussian error.
Bipartite graphs with more edges than a threshold have positive curvature.
problem Determining the curvature of bipartite graphs based on edge density.
method Using a new formula for Lin--Lu--Yau curvature, the study establishes conditions for bipartite graphs to have positive curvature.
result Bipartite graphs with more edges than the specified threshold have positive Lin--Lu--Yau curvature.
New random forest algorithms for PU learning minimize risk directly.
problem Learning from positive and unlabeled data.
method Recursive greedy risk minimization for decision trees.
result Efficient PU random forest algorithm with robustness and low hyperparameter tuning.
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the …
Improved method for computing Fréchet means on SPD matrices.
problem Computing Fréchet means on the manifold of SPD matrices.
method Random matrix theory-based approach for estimating Fréchet means.
result Significantly outperforms state-of-the-art methods in experiments.
A new matrix concentration inequality for random products of matrices.
problem Understanding the behavior of random matrix products under bounded independent positive semidefinite matrices.
method Developed a non-asymptotic concentration inequality for the product of matrices.
result The inequality provides a bound on the deviation of the matrix product from its expected value.
Random 3-manifolds have small eigenvalues.
problem Understanding the smallest eigenvalues of random 3-manifolds.
method Analyzing the smallest positive eigenvalues of random closed 3-manifolds.
result The smallest positive eigenvalue is bounded by a function of the genus and volume of the 3-manifold.
Optimal dictionaries minimize the average squared error in representing random vectors.
problem Finding optimal dictionaries for minimizing ℓ2-norm of coefficients in random vector representations. method Using rank-1 decompositions of symmetric positive semidefinite matrices, explicit descriptions and polynomial-time algorithms for ℓ2-optimal dictionaries are provided. result Explicit descriptions and polynomial-time algorithms for ℓ2-optimal dictionaries are provided. Paper solves open question about non-positive kernels by decomposing them into PD kernels.
problem Can non-positive definite kernels be decomposed into the difference of two positive definite kernels?
method Introduced signed measure to transform positive decomposition into measure decomposition, providing a sufficient and necessary condition.
result First random features algorithm for unbiased estimation of non-positive kernels.
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
problem The puzzling positive correlation between upstreamness and downstreamness in industries and countries.
method Analysis of a simple model of random Input/Output tables and experiments on empirical data.
result Upstreamness and downstreamness of the same industrial sector/country are positively correlated with a slope close to +1.
New method uses manifold learning to infer latent positions of 1D submanifolds in random dot product graphs.
problem Inference on latent positions of unknown 1D submanifolds in RDPGs.
method Apply Isomap for manifold learning to estimate arc lengths on the unknown submanifold.
result Test statistics based on Isomap converge to known submanifold power as auxiliary vertices increase.
A 3-manifold is Haken if it contains a topologically essential surface. The Virtual Haken Conjecture posits that every irreducible 3-manifold with infinite fundamental group has a finite cover which is Haken. In this paper, we study random 3-manifolds and their finite covers in an attempt to shed light on this difficul…
New model tackles PU data with better accuracy.
problem Addressing positive and unlabeled data challenges.
method Double Exponential Tilting Model (DETM)
result DETM effectively handles selected at random PU data.
New method recovers graph latent positions under edge differential privacy.
problem Recovering latent graph information from privatized graphs.
method Applying geometric insights to adjust statistical inference for privatized graphs.
result Achieves consistent recovery of latent positions under local edge differential privacy constraints.
A version of indifference valuation of a European call option is proposed that includes statistical regularities of nonstochastic randomness. Classical relations (forward contract value and Black-Scholes formula) are obtained as particular cases. We show that in the general case of nonstochastic randomness the minimal …
A method uses decision trees to detect and characterize positivity violations in causal inference.
problem Detecting and characterizing positivity violations in causal inference datasets.
method Decision trees dividing covariate space into regions for automatic detection of subspaces violating positivity.
result Scalable and interpretable characterization of subspaces with positivity violations.
The paper equidistributes zeros of random polynomials and sections on manifolds.
problem Equidistribution of zeros of random polynomials and sections on manifolds.
method Weighted pluripotential theory, asymptotic Bernstein-Markov measures, variance estimation.
result Equidistribution holds for non-i.i.d. random coefficients and non-homogeneous manifolds.
Positive weights improve kernel quadrature's accuracy.
problem Improving kernel quadrature weights to be positive and stable.
method Using convex geometry to approximate the kernel mean embedding with positive weights.
result Positive weights lead to improved kernel quadrature bounds with Monte-Carlo-beating rates.
New string kernels discover global properties through random feature maps, avoiding quadratic complexity.
problem Existing string kernels struggle with capturing long patterns, maintaining positive definiteness, and handling large datasets efficiently.
method Proposes a new class of global string kernels using random feature maps to discover global properties through global alignments, ensuring positive definiteness and linear computational cost.
result Random String Embeddings (RSE) achieve better or comparable accuracy to state-of-the-art methods, especially for longer strings.
The paper constructs noncompact hyperbolic surfaces with uniform spectral gaps using random graph models.
problem Building noncompact hyperbolic surfaces with uniform spectral gaps.
method Introduced a random graph model Fχ,n to construct expanding families of graphs, then applied these families to create hyperbolic surfaces. result Explicitly constructed an expanding family of graphs in the critical regime, leading to a sequence of complete, noncompact hyperbolic surfaces with uniformly positive spectral gaps.
Test verifies if data meets SCAR assumption for PU learning.
problem Verify if labeling mechanism follows SCAR assumption in PU learning.
method Generate artificial labels, mimic distribution of test statistic.
result Test detects deviations from SCAR and controls type I error.
A new algorithm FastGM speeds up generating Gumbel-Max variables.
problem Efficiently generating multiple Gumbel-Max variables from high-dimensional vectors.
method FastGM reduces time complexity from O(kn+) to O(klnk+n+) by generating variables in descending order. result Significantly reduces computation time for generating k Gumbel-Max variables. Tests if vertices in graphs have the same latent positions.
problem Testing equality of latent positions in random graphs.
method Empirical Mahalanobis distances from spectral embeddings.
result Test statistics follow chi-square distributions under null and local alternatives.
Study on volumes of random inscribed polytopes in projective geometries.
problem Estimating volumes of random inscribed polytopes in projective geometries.
method Central limit theorems and normal approximation for volumes and dual volumes of random inscribed polytopes.
result Established central limit theorems and normal approximation for volumes and dual volumes of random inscribed polytopes.
Geometric quantization results for Riemann surfaces with semi-positive line bundles.
problem Analyzing geometric quantization for Riemann surfaces with semi-positive line bundles.
method Exploring the Bergman kernel expansion and related results for induced Fubini-Study metrics, Toeplitz operators, and holomorphic torsion.
result Asymptotic results for holomorphic torsion and random sections.
Random features can't explain neural networks' success.
problem Understanding neural networks' empirical success.
method Analyzing random features' limitations on learning neural networks.
result Random features can't learn even a single ReLU neuron with standard Gaussian inputs.
Distributional lattices on Riemannian symmetric spaces are studied, leading to new insights on random walks.
problem Understanding distributional lattices on Riemannian symmetric spaces.
method Introduced distributional lattices, used amenability equivalence, and developed graph speed for Poisson-Voronoi tessellations.
result Simple random walk on distributional lattices in nonamenable spaces has positive embedded speed.
New spectral mixture representation for isotropic kernels simplifies random Fourier features.
problem Applying Random Fourier Features to complex kernels.
method Decompose isotropic kernels into scale mixtures of α-stable random vectors.
result Constructive spectral sampling formula for various kernels.
CLuP achieves near optimal ground state energies for positive and negative Hopfield models.
problem Finding near optimal ground state energies for positive and negative Hopfield models.
method Controlled Loosening-up (CLuP) algorithm with fully lifted random duality theory (fl RDT).
result Achieves ground state free energies of 1.77 and 0.33 for positive and negative Hopfield models respectively. The paper studies the distribution of random degeneracy sets on complex manifolds.
problem Distribution of random degeneracy sets on compact Kähler manifolds.
method Asymptotic expansion of induced Grassmannian Chern forms, meromorphic transforms, and Wishart distribution.
result Normalized currents converge to curvature forms with quantitative estimates.