Research
On-device research index

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

Trend · papers per month

3707411,1111,481 · Jun 202019922001200920172026
48 results for factored tie model

Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural networ…

2017-10-11abs ↗pdf ↗

TIE framework detects out-of-distribution samples and estimates uncertainty without external datasets.

problem Detecting and estimating uncertainty for out-of-distribution samples in neural networks.
method TIE framework extends a classifier to an (n+1)-class model, iteratively refining through training, inversion, and exclusion.
result Unified and interpretable framework for robust anomaly detection and calibrated uncertainty estimation.

Researchers create coordinates for hyperbolic surfaces, proving a magic formula.

problem Constructing coordinates for hyperbolic structures on genus-2 surfaces.
method Developed Fenchel-Nielsen coordinates and Wolpert's magic formula analogues.
result Found Darboux charts for the Goldman symplectic form on branched hyperbolic structures.

Multi-modal data collections, such as corpora of paired images and text snippets, require analysis methods beyond single-view component and topic models. For continuous observations the current dominant approach is based on extensions of canonical correlation analysis, factorizing the variation into components shared b…

2012-10-16abs ↗pdf ↗

In this paper, we construct two families of satellite constructions for Brunnian links, called the satellite sum and the satellite tie. An interesting fact is that by applying the satellite sum and the satellite tie constructions, we can build infinitely many new Brunnian links from any given Brunnian links. With the h…

2019-06-04abs ↗pdf ↗

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first o…

2017-05-23abs ↗pdf ↗

This paper gives mathematical models for flat knotted ribbons, and makes specific conjectures for the least length of ribbon (for a given width) needed to tie the trefoil knot and the figure eight knot. The first conjecture states that (for width one) the least length of ribbon needed to tie an open-ended trefoil knot …

2004-03-02abs ↗pdf ↗

Systemic risks of default contagion in the Russian interbank market are investigated. The analysis is based on considering the bow-tie structure of the weighted oriented graph describing the structure of the interbank loans. A probabilistic model of interbank contagion explicitly taking into account the empirical bow-t…

2014-09-03abs ↗pdf ↗

Business cycles affect startup valuations, both directly and indirectly.

problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.

Characterizes Lebesgue points using nearest neighbor methods.

problem Consistency of classification algorithms based on nearest neighbors.
method Characterization of Lebesgue points via 1-Nearest Neighbor regression.
result Proves convergence of 1-Nearest Neighbor classification algorithms in metric spaces.

Modelling of contagion in interbank networks is discussed. A model taking into account bow-tie structure and dissasortativity of interbank networks is developed. The model is shown to provide a good quantitative description of the Russian interbank market. Detailed arguments favoring the non-percolative nature of conta…

2014-10-01abs ↗pdf ↗

Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evolution for tuning hyperparameters of supervised learning algorithms for…

2019-04-15abs ↗pdf ↗

In this paper the problem of optimal derivative design, profit maximization and risk minimization under adverse selection when multiple agencies compete for the business of a continuum of heterogenous agents is studied. The presence of ties in the agents' best-response correspondences yields discontinuous payoff functi…

2011-07-05abs ↗pdf ↗

Alternative approach to generative modeling using convex conjugates and optimal transport.

problem Traditional generative modeling splits sampling and mapping; this work explores an alternative.
method Inspired by moment measures, proposes a new factorization and uses optimal transport for recovery.
result Intuitive results on factorized distributions, showing potential for practical tasks.

What length of rope (of given diameter) is required to tie a particular knot? To answer this question, we define some new notions of thickness for a space curve, one based on Gromov's distortion, and another generalizing the thickness of Litherland, Simon et al. We prove a basic inequality between these thickness measu…

1997-02-04abs ↗pdf ↗

Alternating-sign Hopf plumbing along a tree yields fibered alternating links whose homological monodromy is, up to a sign, conjugate to some alternating-sign Coxeter transformation. Exploiting this tie, we obtain results about the location of zeros of the Alexander polynomial of the fibered link complement implying a s…

2015-06-05abs ↗pdf ↗

Establishes equivalence between models of derived stacks.

problem Tackles the equivalence between different models of derived geometry.
method Uses Quillen equivalence to show categories of higher derived stacks are equivalent.
result Shows equivalence among models of derived manifolds, Carchedi-Roytenberg, Behrend-Liao-Xu, and Alexandrov-Kontsevich-Schwarz-Zaboronsky.

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that ar…

2019-05-06abs ↗pdf ↗

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international …

2011-07-28abs ↗pdf ↗

Study shows flash crashes in finance are self-organized criticality events.

problem Understanding and predicting anomalous price events in high-frequency finance.
method Investigated volume distributions during flash crashes and linked them to self-organized criticality.
result Volume distributions during flash crashes indicate a diverging second moment, suggesting self-organized criticality.

Isotonic regression binning affects calibration statistics of machine learning models.

problem Isotonic regression binning introduces aleatoric uncertainty in calibration statistics.
method Calibration error statistics are recalibrated using isotonic regression, which produces stratified uncertainties.
result Stratified uncertainties lead to significant differences in bin-based calibration statistics.

Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input. However calculating the precise probability using combinatorics is an intractable problem, so instead we approximate it. Monte Carlo simulation is an effective technique that can be used to approx…

2018-08-22abs ↗pdf ↗

Approximates discounted moments for financial products using polynomial expansions.

problem Approximating discounted moments of stochastic processes for financial applications.
method High-order power series expansion of the infinitesimal generator.
result Error decreases to around 10 to 100 times machine precision for higher orders.

We view strict ring spectra as generalized rings. The study of their algebraic K-theory is motivated by its applications to the automorphism groups of compact manifolds. Partial calculations of algebraic K-theory for the sphere spectrum are available at regular primes, but we seek more conceptual answers in terms of lo…

2014-03-24abs ↗pdf ↗

The abstract discusses parallels between Galois theory and Stone-Weierstrass theorem in various fields.

problem Connecting distinguishing power and expressive power in different fields.
method Elementary theorem connecting distinguishing power and expressive power.
result Foundational principle in linguistics linking distinguishing power and expressive power.

Study of Milnor invariants and ropelength of spherical links.

problem Understanding the relationship between the thickness of spherical links and their Milnor invariants.
method Generalized Massey products and Milnor invariants to spherical links, finding optimal asymptotic bounds.
result Optimal asymptotic bounds on Milnor invariants in terms of thickness, revealing a polynomial vs exponential regime.

In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…

2019-06-12abs ↗pdf ↗

Introduces P-tensors for generalized higher-order message passing in graph neural networks.

problem Expanding the expressive power of graph neural networks through higher-order structures.
method Introduces P-tensors to define the most general form of permutation equivariant message passing.
result Achieves state-of-the-art performance on molecular datasets.

Bayesian neural networks improve deep learning's accuracy and uncertainty estimation.

problem Overconfident predictions, adversarial attacks, and variability underestimation in deep models.
method Stochastic relaxation of feed-forward rectified neural networks with sparsity-promoting priors and Polya-Gamma data augmentation.
result Improved scalability and robustness to architectural design through approximate variational inference.

Fixed angles of convex polygons lead to combinatorially rich polytopes.

problem Understanding the structure of convex polygons with fixed vertex angles.
method Combining combinatorial and geometric approaches, including dual polytopes and Schwarz-Christoffel maps.
result Fixed-angles polytopes are dual to cyclic polytopes under certain conditions.