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.
Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially l…
New model predicts sales of new products with short life cycles.
Exponentially fast SMF algorithm for multi-class classification.
Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter (DEKF) to factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through numerical experiments on synthetic and on real-…
We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading c…
NSGD-M optimizes machine learning models without hyperparameter tuning, even under relaxed smoothness.
The recent financial crisis has led to so-called multi-curve models for the term structure. Here we study a multi-curve extension of short rate models where, in addition to the short rate itself, we introduce short rate spreads. In particular, we consider a Gaussian factor model where the short rate and the spreads are…
Develops polynomial diffusion models for multi-factor commodity futures dynamics.
Exponential family extensions of principal component analysis (EPCA) have received a considerable amount of attention in recent years, demonstrating the growing need for basic modeling tools that do not assume the squared loss or Gaussian distribution. We extend the EPCA model toolbox by presenting the first exponentia…
A new kernel improves tensor classification accuracy and reduces computation time.
A new machine learning model uses matrix exponentials for universal approximation.
Generative models unify heterogeneous data for multimodal fusion.
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. Bu…
FMDP-BF algorithm improves RL in factored MDPs with exponential regret reduction.
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discr…
Paper tackles fair low-rank approximation and column subset selection.
In this paper we see the evolution of a capitalized financial event e, with respect to a capitalization factor f, as the exponential map of a suitably defined Lie group G(f,e), supported by the half-space of capitalized financial events having the same capital sign of e. The Lie group G(f,e) depends upon the capitaliza…
In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for …
Optimizes portfolios with constraints and stochastic factors, deriving explicit solutions.
Proposes iCaRL for nonlinear OOD generalization in causal settings.
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noi…
We consider an economic agent (a household or an insurance company) modelling its surplus process by a deterministic process or by a Brownian motion with drift. The goal is to maximise the expected discounted spendings/dividend payments, given that the discounting factor is given by an exponential CIR process. In the d…
Optimal insurance and investment strategy under exponential preferences in a correlated market model.
We found that factors decay over time, with momentum fitting best.
Sharp large deviations and Gibbs conditioning for portfolio credit risk models.
Quantum networks offer exponential communication savings for large machine learning models.
The paper rethinks the use of exponential averaging in machine learning optimization.
Quantum algorithm speeds up learning from big data exponentially.
New gradient coding schemes reduce decoding error in both random and adversarial straggler settings.
Machine learning factors outperform traditional portfolio optimization methods.
Paper proposes a mean-field gradient descent for zero-sum games, proving convergence to Nash equilibrium.
Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.
We consider the problem of valuing a European option written on an asset whose dynamics are described by an exponential Lévy-type model. In our framework, both the volatility and jump-intensity are allowed to vary stochastically in time through common driving factors -- one fast-varying and one slow-varying. Using Four…
We consider utility maximization problem for semi-martingale models depending on a random factor . We reduce initial maximization problem to the conditional one, given , which we solve using dual approach. For HARA utilities we consider information quantities like Kullback-Leibler information and Hellinger inte…
High-dimensional neural network manifolds misalign with human perception, causing adversarial examples.
Novel method for estimating currency option parameters with improved accuracy.
SGD with machine learning noise converges to global minimum exponentially fast.
AdaX improves Adam by exponentially accumulating past gradients, leading to better performance in machine learning tasks.
New algorithm speeds up polynomial kernel approximations.
We discuss the equivalence between the categories of certain ribbon graphs and subgroups of the modular group and use it to construct exponentially large families of not Hurwitz equivalent simple braid monodromy factorizations of the same element. As an application, we also obtain exponentially large families of {\…
We consider the problem of predicting as well as the best linear combination of d given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination. When the input distribution is known, there already exists an algorithm having an expected excess…
Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dep…
New algorithm reduces feature count and accelerates error convergence.
New algorithms improve approximation of matrix norms, with applications in statistics and machine learning.
Paper shows SVM can achieve super fast convergence rates.
This paper develops a fast algorithm for solving nonlinear PDEs using sparse Cholesky factorization.
Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-e…
In an incomplete market, with incompleteness stemming from stochastic factors imperfectly correlated with the underlying stocks, we derive representations of homothetic (power, exponential and logarithmic) forward performance processes in factor-form using ergodic BSDE. We also develop a connection between the forward …