arXiv research

Unified technique for sequential estimation of convex divergences.

Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…

We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters …

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Proce…

Enhances fairness in multi-output models using optimal transport.

The paper improves semi-supervised learning using $f$-divergences and $α$-Rényi divergences.

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood …

It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model. Noise injection in the distributed setup is a straightforward technique and it represents a promising approach to improve the locally trained models. We investigate…

This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multivariate Gaussian processes from the circulant embedding literature. The method can be performed in $\mathcal{O}(n\log_2 n)$ operations, where…

Paper develops fast, flexible Hawkes process inference for space-time data.

Survey of methods to calibrate neural network predictions.

This paper reviews methods for interpreting deep learning models with sequential data.

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates. In particular…

Majorizing measures control sequential complexities for online learning.

Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.

Paper connects neural networks to Gaussian processes for understanding double-descent.

A scalable method for heavy-tailed gradients using cheap stochastic sub-processes.

Study uniform learnability of binary classification networks with communication.

In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine epidemic spreading with individuals' on-line self-consultation behaviors. An epidemic spreading prediction model is established based on the SEIS-A framework. The prediction process contains two phases. In …

Study evaluates fairness of machine learning models on Kaggle and finds some optimization techniques can induce unfairness.

A new method for accurately reconstructing signals without knowing the kernel or signal regularity.

Study risk-controlling prediction sets for single trajectory data from dynamical systems.

Bayesian nonparametrics improves data-driven risk optimization under distributional uncertainty.

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation work…

We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…

New model estimates higher-order interactions in stochastic processes using lower-dimensional projections.

SGPA calibrates transformer uncertainty for safety-critical tasks.

A scalable method for heavy-tailed data using robust aggregation.

New statistical mechanics analysis shows edge pruning outperforms node pruning in neural networks.

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis. We show how time-frequency analysis and nonnegative matrix factorisation can be jointly formulated as a spectral mixture Gaussia…

Popular deep learning uncertainty estimation methods often mislead on out-of-distribution data.

Generative models solve medical imaging inverse problems without needing paired data.

Framework purifies approximate differential privacy to pure differential privacy.

Solves memorization in diffusion models for manifold data.

This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empirical risk minimization is based on the fact that for a "well-behaved" stochastic process $\left\{ f(X), \ f\in \mathcal F\right\}$ indexed b…

Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean fu…

In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i.e. functionals of the training data with low variance that take the form of averages over $k$-tuples. From a computational perspective, …

This work introduces a new data-driven estimator for the Bayesian Cramér-Rao bound using score matching.

Proposes a method to optimize neural network initialization using marginal likelihood maximization.

A new method for faster prediction in distributed Gaussian processes.

Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling tra…

Novel deep Gaussian process improves predictive uncertainty.

The paper proposes a new method for density estimation using spline quasi-interpolation for clustering.

New method for PU learning with instance-dependent propensity scores.

Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory. In this paper, we propose a new analytic paradigm …

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…

Calibrating a Lévy process usually requires characterizing its jump distribution. Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D. For multivariate Lévy processes and less smooth …