arXiv research

Cluster Quilting clusters fragmented data sets for neuroscience and genomics.

New algorithm achieves optimal clustering for sparse centers with high dimensions.

Paper proposes a robust method for federated ICA with geometric median aggregation.

Randomized spectral co-clustering speeds up large-scale directed networks.

Higher-order motif structures and multi-vertex interactions are becoming increasingly important in studies that aim to improve our understanding of functionalities and evolution patterns of networks. To elucidate the role of higher-order structures in community detection problems over complex networks, we introduce the…

New method clusters high-dimensional data with anisotropic noise.

Spectral clustering for directed graphs using likelihood estimation.

Clustering algorithms are used in a large number of applications and play an important role in modern machine learning-- yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised learning. In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for c…

Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people who communicate with each other, are one example. Communities or clusters of highly connected actors form an essential feature in the structure of several emp…

Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point…

New model for clustering dependent community Hawkes processes in temporal networks.

The paper models SOFR and EFFR dynamics, reconciling diffusive and piecewise paths.

There are more than eight hundred interest rates published in China bond market every day. Which are the benchmark interest rates that have broad influences on most interest rates is a major concern for economists. In this paper, multi-variable Granger causality test is developed and applied to construct a directed net…

The study proposes algorithms to minimize rating discordance in missing data.

Overrides of credit ratings are important correctives of ratings that are determined by statistical rating models. Financial institutions and banking regulators agree on this because on the one hand errors with ratings of corporates or banks can have fatal consequences for the lending institutions and on the other hand…

Model credit ratings using economic states with Markov chains.

The paper analyzes how learning rate affects SGD and provides insights into optimal rates.

This paper models short rates with jumps using PDEs.

We first show that there are in fact triangular arbitrage opportunities in the spot foreign exchange markets, analyzing the time dependence of the yen-dollar rate, the dollar-euro rate and the yen-euro rate. Next, we propose a model of foreign exchange rates with an interaction. The model includes effects of triangular…

In this survey paper we discuss recent advances on short interest rate models which can be formulated in terms of a stochastic differential equation for the instantaneous interest rate (also called short rate) or a system of such equations in case the short rate is assumed to depend also on other stochastic factors. Ou…

A novel approach models rating transitions using Lie groups and Deep Learning.

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference in…

Following widely used in visual recognition concept of relative attributes, the article establishes definition of the relative PCA attributes for a class of objects defined by vectors of their parameters. A new rating model (RELARM) is built using relative PCA attribute ranking functions for rating object description a…

Paper examines pricing and hedging for cross-currency swaps referencing backward-looking rates.

Method calibrates local volatility and stochastic short rate models for equity-rate dynamics.

This paper analyzes the robust growth rate of leveraged ETFs under uncertain parameters.

Examines SOFR derivatives pricing and hedging post-LIBOR discontinuation.

Approximates bond option volatilities using affine short-rate models.

In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instead of l1-mixed-norm regularization. We utilize localization techniques to obtain the sharp learning rate. The bound is characterized by the d…

The currency carry trade is the investment strategy that involves selling low interest rate currencies in order to purchase higher interest rate currencies, thus profiting from the interest rate differentials. This is a well known financial puzzle to explain, since assuming foreign exchange risk is uninhibited and the …

We construct a no-arbitrage model of bond prices where the long bond is used as a numeraire. We develop bond prices and their dynamics without developing any model for the spot rate or forward rates. The model is arbitrage free and all nominal interest rates remain positive in the model. We give examples where our mode…

This paper shows how forward rate interpolations are equivalent to discount factor interpolations in yield curve construction.

The paper introduces a new short rate model with memory components.

Rate-In dynamically adjusts dropout rates during inference to improve uncertainty estimation in neural networks.

We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …

Clarifies interest rate cap rules for loans with unconventional cash flows.

Cyclical learning rate improves neural machine translation performance.

Proves lower discount rates are needed for future losses.

Developed unbiased estimators for Heston model with stochastic interest rates.

We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and Härtel [2014], and El Karoui et a…

Rate GENERIC extends thermodynamics principles to non-equilibrium systems.

Two-dimensional transition rates improve life insurance reserve calculations.

Stochastic gradient descent with a large initial learning rate is widely used for training modern neural net architectures. Although a small initial learning rate allows for faster training and better test performance initially, the large learning rate achieves better generalization soon after the learning rate is anne…

This work models overnight rates with jumps and discontinuities, extending classical short-rate models.

GALA adapts learning rates online by aligning gradients, improving deep learning model performance.

The paper tackles fast rates in structured prediction problems.

Paper studies Gaussian approximation in linear regression with rates derived.

Defines a new short rate model and convexity adjustment formulae.