arXiv research

DualIV simplifies non-linear IV regression via dual formulation.

Paper analyzes how contrastive learning structures learned representations.

Paper proposes OKGAN to improve GAN training, addressing mode collapse and cycling.

Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robust…

Transformer's attention mechanism is re-examined using kernel smoothing.

Mathematical analysis improves SGMs, resolving memorization issues.

DKGM improves image quality by debiasing kernel-based models.

Paper introduces EO_k for quantifying accuracy-fairness trade-offs in FRL.

Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing. Current NMF techniques have been limited to a single-objective problem in either its linear or nonlinear kernel-based formulation. In this…

Paper introduces new regression methods for consistent estimation of biophysical parameters.

Study local convergence of GDA for training GANs with kernel-based discriminators.

This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors. Expe…

Improved particle-flow event reconstruction for future colliders using scalable neural networks.

Paper shows robustness of kernel-based pairwise learning without strict assumptions.

New algorithm improves dynamic mode decomposition for high-dimensional data.

Kernel-based function approximation improves reinforcement learning performance.

Develops a new method for learning non-parametric DAGs using RKHS.

Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem …

Study provides guarantees for kernel clustering under non-parametric mixtures.

Kernel-based algorithms improve integral estimation with near-geometric speed.

Kernel-based L2-boosting with structure constraints improves regression efficiency.

Novel confidence intervals improve convergence rates for sparse kernel-based models.

Paper develops a method to highlight influential transitions for off-policy evaluation.

Adaptive rule improves kernel-based gradient descent performance.

Paper introduces MinDiff framework for balancing classifier performance and fairness.

ConvNets can be translated into CKNs that perform similarly.

Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutio…

New method for causal inference with complex treatment compositions.

New theoretical tools simplify kernel-based tests analysis.

A new kernel-based CI test improves on existing methods.

We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) problem using operator-valued kernels. We show that some of the existing formulations of this problem are special cases of our framework. We then propose a c…

Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing. Being generative rather than discriminative models, however, their classification performance is a drawback. In this paper we apply ideas fr…

Laplace kernel feature selection offers statistical guarantees for nonparametric models with few samples.

Optimal kernel improves estimation accuracy in modal statistical methods.

Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.

Prediction of dynamical time series with additive noise using support vector machines or kernel based regression has been proved to be consistent for certain classes of discrete dynamical systems. Consistency implies that these methods are effective at computing the expected value of a point at a future time given the …

Paper optimizes portfolios for absolute return funds with constraints.

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective …

Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-applicable classes of nonlinear functions can be approximated with desired accuracy. Nevertheless, inherent to the nonparametric nature of kernel…

FastKCI speeds up KCI tests for causal inference on large datasets.

Paper proposes an efficient causal discovery method with linear computational complexity.

New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.

Framework identifies causal direction from single data setting.

Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel based methods for additive models. These learning rates compare favourably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel …

Quantum machine learning for 2D classification tasks using optimized feature maps.

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter op…

Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior information on the unknown process is enforced. This reduces the variance of the estimates and thus makes kernel-based methods particularly attract…

Kernel based methods have shown effective performance in many remote sensing classification tasks. However their performance significantly depend on its hyper-parameters. The conventional technique to estimate the parameter comes with high computational complexity. Thus, the objective of this letter is to propose an fa…