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.
Combines fast evaluation with Bayes consistency in nearest neighbors.
Paper presents a fast algorithm for pricing Bermudan swaptions under the two-factor Hull-White model.
The paper analyzes reinforcement learning methods for estimating weights and quality functions with fast convergence rates.
Paper offers a fast convergence theory for offline decision making.
Fast BATLLNN speeds up verification of TLL NNs by 400x.
The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of DNNs. These methods need sufficient evaluation data to approximate and minimize the validation error function of hyperparameters. However, …
Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimation optimality for any input distribution, cross-validating for an optimal parameter requires significant computation do…
DPM-Solver speeds up DPM sampling to 10-20 function evaluations.
A fast method for LOOCV in k-NN regression reduces computation time.
Limbo is an open-source C++11 library for Bayesian optimization which is designed to be both highly flexible and very fast. It can be used to optimize functions for which the gradient is unknown, evaluations are expensive, and runtime cost matters (e.g., on embedded systems or robots). Benchmarks on standard functions …
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when , is not useful as a feature to predict , as long as is also a regressor. On the contrary, if $P(X \mid Y, Z) \neq P(X…
Bayesian REX learns Atari games from demonstrations efficiently.
The paper addresses numerical integration issues in SV models, proposing a fast regime switching algorithm.
A fast method learns plasma collision kernels from simulations, improving kinetic models.
In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in th…
Paper compares ML models for fast power system contingency case identification.
Fast feature selection for SHM using canonical correlation.
Post-process Bayesian inference speeds up posterior approximation.
In this paper, we derive the price of a European call option of an asset following a normal process assuming stochastic volatility. The volatility is assumed to follow the Cox Ingersoll Ross (CIR) process. We then use the fast Fourier transform (FFT) to evaluate the option price given we know the characteristic functio…
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide…
Bayesian optimization for long-term outcomes using fast and slow experiments.
Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a…
This work learns effective dynamics from short-term data of stochastic systems.
Improved barrier option pricing in Heston model using COS-BEM method.
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial …
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data. However, as GNNs typically define the graph convolution by the orthonormal basis for the graph Laplacian, they suffer from hig…
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K pro…
Fast algorithms developed for adaptive and fully adaptive submodular maximization problems.
Fastest video anomaly detection via teacher-student distillation.
A new knot invariant is fast, strong, topologically meaningful, and fun.
GeoStat simplifies time series classification with fast, intuitive features.
A new SINC method for fast and accurate option pricing.
We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the target density to speculatively evaluate many potential future steps of the chain in parallel. The approach can accelerate computation of t…
Characteristic functions of several popular classes of distributions and processes admit analytic continuation into unions of strips and open coni around . The Fourier transform techniques reduces calculation of probability distributions and option prices to evaluation of integrals whose i…
FAWMF adapts weights for implicit feedback recommendation efficiently.
PyBADS optimizes complex functions quickly and reliably.
Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. Despite much attention, however, progress towards more robust models is significantly impaired by the difficulty of evaluating the robustness …
CLASSIX is a fast and explainable clustering method that sorts data and merges groups.
Fast and accurate methods for low-rank learning problems.
Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventional methods suffer from excessive usage of memory and slow response times. We propose a method where multiple random projection trees are c…
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new…
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations base…
AI agents improve forecast combination in empirical economics.
We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal. First, we study the clas…
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-ser…
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
Aims to create a world model without baggage, achieving good performance.