We prove that simple, thick hyperbolic P-manifolds of dimension >2 exhibit Mostow rigidity. We also prove a quasi-isometry rigidity result for the fundamental groups of simple, thick hyperbolic P-manifolds of dimension >2. The key tool in the proofs of these rigidity results is a strong form of the Jordan separation th…
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48 results for “strong separation”
We provide a strengthening of Jordan separation, to the setting of maps from a compact topological space X into a sphere, where the source space X is not necessarily a codimension one sphere, and the map is not necessarily injective.
Improves arc separation result for homogeneous spaces.
problem Separating regions in homogeneous spaces by arcs.
method Using homogeneity instead of strong local homogeneity, and considering arcs with one interior point.
result Regions in homogeneous spaces of dimension ≥ 2 are not separated by arcs.
We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of classes and lie in the -dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linear…
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particula…
A new knot invariant is fast, strong, topologically meaningful, and fun.
problem Computing and understanding knot invariants efficiently and comprehensively.
method Developed a pair of polynomial knot invariants Θ=(Δ,θ) that are fast, strong, and topologically meaningful.
result Θ is a powerful knot invariant with separation power greater than other known invariants.
On the Slope of Hyperelliptic Lefschetz Fibrations and the Number of Separating Vanishing Cyclesmath.SG
In this article we find an upper and lower bound for the slope of genus g hyperelliptic Lefschetz fibrations, which is sharp when g = 2, and demonstrate the strong connection, in general, between the slope of hyperelliptic genus g Lefschetz fibrations and the number of separating vanishing cycles. Specifically, we show…
Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separat…
Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspectivecs.LG
New algorithm achieves small-loss bounds in online learning with improved rates.
problem Achieving strong stability in online learning algorithms.
method Introduces -separation to enforce strong stability, unifying previous approaches.
result Oracle-efficient algorithm achieves small-loss bounds with improved rates.
Geometric proof of contractibility of unitary group in strong topology.
problem Contractibility of unitary group in strong operator topology.
method Direct geometric proof and construction of special subspaces and operators.
result Direct geometric proof of contractibility theorem.
Kernel k-Means algorithm improves clustering of non-linear data.
problem Non-convexity of kernel k-Means objective function leads to local minima.
method Generalizes MM approach to solve non-convex problem in kernel and multi-kernel settings.
result Establishes strong consistency guarantees for Kernel Power k-Means.
New method improves source separation using adversarial NMF.
problem Improving source separation in single channel signals.
method Adversarial training of non-negative matrix factorization (NMF).
result Adversarial NMF leads to better signal reconstruction.
We consider the online multiclass linear classification under the bandit feedback setting. Beygelzimer, Pál, Szörényi, Thiruvenkatachari, Wei, and Zhang [ICML'19] considered two notions of linear separability, weak and strong linear separability. When examples are strongly linearly separable with margin , they prese…
Study shows systole behavior changes significantly for large genus hyperbolic surfaces.
problem Understanding systole behavior in large genus hyperbolic surfaces.
method Analysis of random surfaces with respect to Weil-Petersson volume.
result Expected value of separating systole behaves like for large genus.
New kernels defined for various spaces, including measures.
problem Defining kernels on non-standard spaces like measures.
method Integrally strictly positive definite and characteristic kernels on Hilbert, Banach, and metric spaces.
result Explicit classes of kernels on spaces and sets of measures.
New algorithm recovers mixture means even with many outliers.
problem Estimating mixture means when outliers overwhelm small groups.
method Proposes an algorithm for robust mixture learning with minimal list-size overhead.
result Order-optimal error guarantees for each mixture mean.
This paper strengthens the computational separation between multimodal and unimodal learning, showing unimodal learning is hard on typical instances.
problem Theoretical justification for empirical success of multimodal machine learning.
method Introduced a stronger average-case computational separation between unimodal and multimodal learning.
result For typical instances, unimodal learning is computationally hard, while multimodal learning is easy.
New method improves source separation using NMF and adversarial learning.
problem Source separation in single channel data.
method Maximum Discrepancy Generative Regularization applied to NMF.
result Improvement in reconstructed signals, especially in weak supervision scenarios.
Based on the tick-by-tick stock prices from the German and American stock markets, we study the statistical properties of the distribution of the individual stocks and the index returns in highly collective and noisy intervals of trading, separately. We show that periods characterized by the strong inter-stock coupling…
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
problem Signal cancellation and off-support extrapolation in additive models.
method Tensor Separation Learning (TSL) via stagewise greedy procedure with orthogonal refitting.
result TSL avoids information loss caused by marginalizing higher-order interactions.
In this paper we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the Diagonal Quadratic Approximation Method (DQAM) of Mulvey and Ruszczyński and the Parallel Coordinate Descent Method (PCDM) of Richtárik and Takáč. We show that …
We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes-consistent in all metric spaces admitting any such learner, making it an "optimistically universal" Bayes-consistent learner. This is the first learning algorithm known to e…
We consider composite loss functions for multiclass prediction comprising a proper (i.e., Fisher-consistent) loss over probability distributions and an inverse link function. We establish conditions for their (strong) convexity and explore the implications. We also show how the separation of concerns afforded by using …
Logistic regression is one of the most popular methods in binary classification, wherein estimation of model parameters is carried out by solving the maximum likelihood (ML) optimization problem, and the ML estimator is defined to be the optimal solution of this problem. It is well known that the ML estimator exists wh…
While there has been much recent progress using deep learning techniques to separate speech and music audio signals, these systems typically require large collections of isolated sources during the training process. When extending audio source separation algorithms to more general domains such as environmental monitori…
Study explores robust Orlicz spaces in finance, showing separability implications.
problem Understanding robustness in financial and economic contexts.
method Distinguished two constructions of robust Orlicz spaces: top-down and bottom-up.
result Separability of robust Orlicz spaces has strong implications for dominatedness and order completeness.
Fine shape Imath.AT
We introduce and develop fine shape, which has a very simple definition and aims to supersede all previously known shape theories for metrizable spaces. The problem with known shape theories of metrizable spaces is illustrated by the following bizarre situation. Čech cohomology is an invariant of shape, and a fortiori …
State-of-art deep neural networks (DNN) are vulnerable to attacks by adversarial examples: a carefully designed small perturbation to the input, that is imperceptible to human, can mislead DNN. To understand the root cause of adversarial examples, we quantify the probability of adversarial example existence for linear …
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending convolution operations to the non-Euclidean geometry. Although various types of convolution operations h…
Grokking occurs in simple binary logistic classification near linear separability and noise.
problem Delayed generalization in binary logistic classification.
method Analytical and empirical insights into gradient descent dynamics near critical points.
result Logistic regression exhibits grokking when training data is nearly linearly separable from the origin with strong noise.
In this paper we study some consequences of the author's classification of graph manifolds by their profinite fundamental groups. In particular we study commensurability, the behaviour of knots, and relation to mapping classes. We prove that the exteriors of graph knots are distinguished among all 3-manifold groups by …
On a Riemannian manifold, lower Ricci curvature bounds are known to be characterized by geodesic convexity properties of various entropies with respect to the Kantorovich-Rubinstein-Wasserstein square distance from optimal transportation. These notions also make sense in a (nonsmooth) metric measure setting, where they…
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on de…
DCC separates marginal estimation from dependence modeling for improved classification accuracy.
problem Classifying with strong dependence between features.
method Deep Copula Classifier using neural copula densities.
result Achieves excess-risk for -smooth copulas.
New algorithm learns mappings between metric spaces, achieving strong consistency.
problem Learning mappings between metric spaces with unbounded loss.
method Metric medoids and semi-stable compression.
result Strong Bayes-consistency for topologically separable spaces and bounded labels.
Przytycki and Sokolov proved that a three-manifold admits a semi-free action of the finite cyclic group of order with a circle as the set of fixed points if and only if is obtained from the three-sphere by surgery along a strongly periodic link . Moreover, if the quotient three-manifold is an integral ho…
Rationality of conformally invariant local correlation functions on compactified Minkowski spacehep-th
Rationality of the Wightman functions is proven to follow from energy positivity, locality and a natural condition of global conformal invariance (GCI) in any number D of space-time dimensions. The GCI condition allows to treat correlation functions as generalized sections of a vector bundle over the compactification o…
Paper investigates separating times for general diffusions, providing new insights.
problem Understanding phase transitions between equivalence and singularity in diffusions.
method Representation of separating time as hitting time of a deterministic set, characterized by speed and scale.
result Explicit and easy-to-check conditions for absolute continuity and singularity of diffusions.
Power Assisted Trend Followingq-fin.TR
Develops a new trend power indicator using DSP techniques.
problem Determining the strength and reversibility of trends.
method Derives a novel indicator using digital signal processing.
result Accuracy of the new indicator correlates with PNL performance.
Multi-armed bandits are a quintessential machine learning problem requiring the balancing of exploration and exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an …
SAL framework uses unlabeled data to improve OOD detection.
problem Lack of clean OOD samples makes OOD detection challenging.
method SAL framework separates candidate outliers and trains an OOD classifier.
result SAL achieves state-of-the-art performance on benchmarks.
The paper proves vanishing bounded cohomology for various groups.
problem Vanishing bounded cohomology for specific groups.
method Elementary algebraic criterion called the commuting cyclic conjugates condition.
result Many groups of interest have vanishing bounded cohomology.
Using Decision Tree Learner to Classify Solvency Position for Thai Non-life Insurance Companiesq-fin.RM
This paper introduces a Decision Tree Learner as an early warning system for classification of the non-life insurance companies according to their financial solid as strong, moderate, weak, or insolvency. In this study, we ran several experiments to show that the proposed model can achieve a good result using standard …
Novel graph-spanning algorithm detects changes in high-dimensional data.
problem Detecting changes in high-dimensional data across various distributions.
method Graph-spanning algorithm designed for offline and online data.
result Achieves high detection power with minimal separation rate.
Develops an algorithm to find the best subset of points for maximizing the coefficient of determination.
problem Finding the optimal subset of points for maximizing the coefficient of determination in robust correlation analysis.
method The extit{quadratic sweep} method, which involves projecting points into \(\mathbb{R}^5\) and iterating over linearly separable \(k\)-subsets.
result The method optimally finds the best subset of points for maximizing the coefficient of determination without error over several million trials up to \(n=30\).
A singularity theorem based on asymptotic volume growth
problem Proving singularity theorems
method Introducing asymptotic volume-expansion invariants
result Proving an explicit upper bound on the time-separation from a hypersurface to its chronological past
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection exists between methods for communication reduction and privacy preservation in the context of distributed machine learning. In particular, we…
Machine learning predicts phase behavior in active matter suspensions.
problem Predicting phase behavior in active matter systems using machine learning.
method Used deep learning techniques, including fully connected networks and graph neural networks, to predict motility-induced phase separation (MIPS) in ABP suspensions.
result Strong agreement between machine learning predictions and MIPS binodal from simulations, suggesting machine learning as an effective method for phase behavior determination.