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.
WIPS optimizes inner product weights to approximate various similarities.
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE). In contrast to IPS, that is limited to approximating positive-definite (PD) similarities, SIPS goes beyond the limitation by introd…
Language-based methods improve human similarity approximations without requiring many human judgments.
Similarity algebra extends algebraic structures with quantitative bounds.
Paper improves image retrieval quality using nonlinear rank approximations.
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding. The inner product similarity (IPS) with feature vectors computed via neural networks is commonly used for representing the strength of association between two nodes. However, only a little work has b…
New method approximates Individual Fairness using human judgments.
We introduce TzK (pronounced "task"), a conditional probability flow-based model that exploits attributes (e.g., style, class membership, or other side information) in order to learn tight conditional prior around manifolds of the target observations. The model is trained via approximated ML, and offers efficient appro…
The paper analyzes the generalization of deep neural networks for metric and similarity learning.
The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field. The asynchronicity of the process is incorporated into the mean field to produce convergence results which remain similar to those of a…
A new method for efficient Gaussian process inference using sparse approximations.
This research proposes a new distance metric using Isolation Forests.
Bing's house-like spines approximate all PL manifolds.
ICQ improves high-dimensional similarity search without sacrificing precision.
FREDE efficiently embeds graphs using linear space and guarantees quality.
We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical analysis using standard notions of graph approximation, significantly …
Can one reduce the size of a graph without significantly altering its basic properties? The graph reduction problem is hereby approached from the perspective of restricted spectral approximation, a modification of the spectral similarity measure used for graph sparsification. This choice is motivated by the observation…
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a t…
Two new algorithms improve federated optimization under second-order similarity.
We provide an explicit aggregation in the neoclassical growth model with aggregate shocks and uninsurable employment risk. We show there are two restrictions on the unemployment shock for approximate aggregation to occur. First the probability of unemployment must be positive for each agent in each time period. That en…
Survey of deep learning methods for graph similarity.
Graphs approximate Laplacian spectra on manifolds.
We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative. When the true number of clusters is supposed to be known, a new covariance-based dissimilarity measure is introduced, from which we obtain the approxima…
New algorithms approximate state similarity in large MDPs.
The method of random projections has become a standard tool for machine learning, data mining, and search with massive data at Web scale. The effective use of random projections requires efficient coding schemes for quantizing (real-valued) projected data into integers. In this paper, we focus on a simple 2-bit coding …
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial …
Extends Tanimoto kernel to real-valued functions.
In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image …
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
Smooth 2-tori in R^4 can be approximated by polyhedral Lagrangian or isotropic tori.
Approaches KL divergence for learning multi-sense word distributions.
We carry out the first main step towards the construction of new examples of complete embedded self-similar surfaces under mean curvature flow. An approximate solution is obtained by taking two known examples of self-similar surfaces and desingularizing the intersection circle using an appropriately modified singly per…
The paper examines how to test if two learning algorithms produce similar outcomes.
We present a new approximation to the normal distribution quantile function. It has a similar form to the approximation of Beasley and Springer [3], providing a maximum absolute error of less than . This is less accurate than [3], but still sufficient for many applications. However it is faster than …
This paper improves spectral clustering for large datasets using the Nystrom method.
The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibl…
Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to pe…
PPC learns binary codes from data similarities and dissimilarities.
We show that shortfall risks of American options in a sequence of multinomial approximations of the multidimensional Black--Scholes (BS) market converge to the corresponding quantities for similar American options in the multidimensional BS market with path dependent payoffs. In comparison to previous papers we conside…
A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data. In this paper, we propose a method that can learn efficiently similarit…
Gaussian random vectors exhibit the loss of dimension phenomena, which relate to their joint survival tail behaviour. Besides, the fact that the components of such vectors are light-tailed complicates the approximations of various multivariate risk measures significantly. In this contribution we derive precise approxim…
Paper presents a new trie for integer sketches to improve similarity searches.
Unified framework for comparing clusterings from information-theoretic and pair-counting perspectives.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
Method quantifies relation similarity using entity pair distributions.
New method reduces clustering time and improves accuracy.
A novel approach to analyzing time series generated by complex systems, such as markets, is presented. The basic idea of the approach is the {\it Law of Self-Similar Evolution}, according to which any complex system develops self-similarly. There always exist some internal laws governing the evolution of a system, say …
LSH methods extend to function spaces for efficient similarity search.