arXiv research

MindFlayer SGD improves parallel SGD for heterogeneous, random compute times.

Linear time algorithm for random walk kernels on sparse graphs.

New methods improve prediction performance and reduce computation time in boosting and random forest models.

ROCKET speeds up time series classification without sacrificing accuracy.

New methods improve Reservoir Computing for chaotic time series prediction.

LightOn OPUs accelerate randomized numerical linear algebra, reducing computational costs.

We introduce a natural generalization of the forward-starting options, first discussed by M. Rubinstein. The main feature of the contract presented here is that the strike-determination time is not fixed ex-ante, but allowed to be random, usually related to the occurrence of some event, either of financial nature or no…

New method uses randomized sparse neural networks to solve time-dependent PDEs more accurately and efficiently.

Framework combines random features with CDEs for efficient time-series learning.

The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time. This class includes Ising models with homogeneous pairwise potentials but arbitrary (inhomogeneous) unary pot…

Accelerates signature kernel computation for sequences.

Reservoir computing's success depends on mapping different input time series to separable states.

SRMD uses random features for efficient time-frequency analysis.

In recent years, dynamically growing data and incrementally growing number of classes pose new challenges to large-scale data classification research. Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased. However, some methods are with weak pr…

A faster graph kernel using optical random features.

Improved MMD test for two-sample testing with random Fourier features.

We derive the exact form of the eigenvalue spectra of correlation matrices derived from a set of time-shifted, finite Brownian random walks (time-series). These matrices can be seen as random, real, asymmetric matrices with a special structure superimposed due to the time-shift. We demonstrate that the associated eigen…

Improved matrix approximation using randomized algorithms.

Manifold Markov chain Monte Carlo algorithms have been introduced to sample more effectively from challenging target densities exhibiting multiple modes or strong correlations. Such algorithms exploit the local geometry of the parameter space, thus enabling chains to achieve a faster convergence rate when measured in n…

We study the generalization properties of ridge regression with random features in the statistical learning framework. We show for the first time that $O(1/\sqrt{n})$ learning bounds can be achieved with only $O(\sqrt{n}\log n)$ random features rather than $O({n})$ as suggested by previous results. Further, we prove fa…

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output spac…

A new sampling strategy for random Fourier features reduces computation time and improves prediction performance.

Optimal transport is #P-hard when components are independent, even with approximate solutions.

Continuous time random walks (CTRWs) are used in physics to model anomalous diffusion, by incorporating a random waiting time between particle jumps. In finance, the particle jumps are log-returns and the waiting times measure delay between transactions. These two random variables (log-return and waiting time) are typi…

MINIROCKET speeds up time series classification by 75x.

Abstract: Nonlinear random walk with distributionally robust transition probabilities.

Time series data analytics has been a problem of substantial interests for decades, and Dynamic Time Warping (DTW) has been the most widely adopted technique to measure dissimilarity between time series. A number of global-alignment kernels have since been proposed in the spirit of DTW to extend its use to kernel-based…

MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learn…

Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such co…

Investigates multifractal scaling in critical dynamics of random surfaces.

New explanation of reservoir computing using random projections.

A new method for efficient nonlinear process monitoring using random Bernoulli features.

A new algorithm FastGM speeds up generating Gumbel-Max variables.

We study an optimal multiple stopping problem for call-type payoff driven by a spectrally negative Levy process. The stopping times are separated by constant refraction times, and the discount rate can be positive or negative. The computation involves a distribution of the Levy process at a constant horizon and hence t…

New model stabilizes asynchronous LTI systems, independent of synchronous stability.

This paper improves computational efficiency in kernel ridge regression under covariate shift.

Rocket algorithm classifies time-series data efficiently using random projections and natural sparsity.

Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual I…

New framework reduces private mean estimation error with optimal efficiency.

Analyzes Lévy flights on manifolds for finding small targets.

The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the inference phase of DNNs. However, it can be hardly used in the training phase because the training phase involves dense matrix-multiplicatio…

Quantum machine learning tackles large datasets with randomized measurements.

WITCHcraft improves PGD attacks with random step size, enhancing efficiency.

Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational co…

HD algorithm simulates dynamics on random matrix ensembles without generating full matrices.

DynForest R package predicts outcomes with time-dependent predictors.

This paper presents a sequential randomized lowrank matrix factorization approach for incrementally predicting values of an unknown function at test points using the Gaussian Processes framework. It is well-known that in the Gaussian processes framework, the computational bottlenecks are the inversion of the (regulariz…

coverforest speeds up conformal predictions for random forests.