Paper introduces MinDiff framework for balancing classifier performance and fairness.
problem Balancing classifier performance and fairness in machine learning models.
method MinDiff framework with kernel-based statistical dependency tests.
result Demonstrates real-world improvements in classifier performance and fairness.
A novel KD classifier improves character recognition performance.
problem Improving character recognition accuracy.
method Kernel-based generative classifier in distortion subspace with iterative kernel selection.
result The KD classifier outperforms existing classifiers and has unique recognition capability.
A fast method for selecting Gaussian kernel bandwidth in kernel-based classifiers.
problem High computational complexity in estimating Gaussian kernel bandwidth.
method Developed based on reproducing kernel Hilbert space operators.
result Proposed method outperforms state-of-the-art methods in computational time and performance.
A new PU classifier PUAL tackles trifurcate data issues.
problem Training classifiers on trifurcate data containing only labeled-positive instances and unlabeled instances.
method PUAL classifier with asymmetric loss and kernel-based algorithm.
result PUAL achieves satisfactory classification on trifurcate data.
A framework assesses the trustworthiness of probabilistic classifiers using local calibration error.
problem Assessing the trustworthiness of probabilistic classifiers beyond traditional metrics.
method I-trustworthy framework linking local calibration to trustworthiness; Kernel Local Calibration Error (KLCE) method for hypothesis testing.
result The effectiveness of the proposed test statistic demonstrated through simulated and real-world datasets.
We consider a problem of risk estimation for large-margin multi-class classifiers. We propose a novel risk bound for the multi-class classification problem. The bound involves the marginal distribution of the classifier and the Rademacher complexity of the hypothesis class. We prove that our bound is tight in the numbe…
The paper develops methods to estimate and assess the risk of binary classification.
problem Estimating the underlying regression function for binary classification.
method Three kernel-based semi-parametric resampling methods are proposed to build confidence regions for the regression function.
result The proposed methods guarantee regions with exact coverage probabilities and are strongly consistent.
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the diss…
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…
Efficient classifier with uncertainty bounds for safety-critical applications.
problem Lack of uncertainty bounds in high-accuracy classifiers for safety-critical tasks.
method Nadaraya-Watson estimator with frequentist bounds.
result Competitive accuracy and uncertainty bounds at reduced computational cost.
Paper shows robustness of kernel-based pairwise learning without strict assumptions.
problem Statistical robustness of kernel-based pairwise learning under minimal conditions.
method No assumptions on input and output spaces; derives influence function and robustness.
result Qualitative robustness of kernel-based estimator established.
Localized Multiple Kernel Learning improves anomaly detection performance.
problem Anomaly detection in one-class classification tasks.
method Localized Multiple Kernel Learning (LMKAD) for One-class Classification (OCC).
result LMKAD achieves significantly better Gmean scores with fewer support vectors.
New algorithm improves dynamic mode decomposition for high-dimensional data.
problem Reduced modeling in high-dimensional spaces.
method Low rank constraint optimization and kernel-based computation.
result Gain in approximation accuracy and computational efficiency.
Kernel-based function approximation improves reinforcement learning performance.
problem Average reward reinforcement learning in infinite horizon settings.
method Optimistic algorithm based on kernel ridge regression.
result No-regret performance guarantees and confidence intervals for kernel-based predictions.
Improves SVM speed by 2 orders of magnitude for 12 out of 17 datasets.
problem Slowness of kernel classifiers like SVM for large problems.
method Piecewise linear classifier trained from kernel-based classifier.
result Improves classification speed by up to 2 orders of magnitude.
Kernel-based methods solve Heath-Jarrow-Morton models with Musiela parametrization.
problem Solving Heath-Jarrow-Morton models with Musiela parametrization.
method Kernel-based collocation methods as Euler-Maruyama approximations of stochastic differential equations.
result Derivation of a rate of convergence bound under specified conditions.
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.
problem Statistical guarantees for kernel-based clustering without strong assumptions.
method Non-parametric mixture models, kernel-based clustering, consistency guarantees.
result Necessary and sufficient separability conditions for consistent clustering recovery.
Kernel-based algorithms improve integral estimation with near-geometric speed.
problem Estimating integrals with target measures that are nearly atomic.
method Weighted kernel herding and sequential Bayesian quadrature.
result Near-geometric rate of convergence for nearly atomic target measures.
Kernel-based L2-boosting with structure constraints improves regression efficiency.
problem Developing efficient kernel methods for regression.
method Kernel-based re-scaled boosting with truncation (KReBooT).
result KReBooT achieves near overfitting resistance and sparse estimates.
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear…
New kernel method for shape classification on Kendall shape space.
problem Classification of shapes on non-Euclidean Kendall shape space.
method Extrinsic Veronese Whitney Gaussian kernel for KRRC on Σ2k. result KRRC classifier performs well on real Kendall shape data.
KCal calibrates deep networks by embedding logits in a metric space.
problem Overconfident predictions from DNNs, especially in high-risk applications.
method KCal learns a metric space on the penultimate-layer latent embedding and generates predictions using kernel density estimates.
result KCal provides a provable full calibration guarantee and consistently outperforms baselines.
Novel confidence intervals improve convergence rates for sparse kernel-based models.
problem High computational cost in kernel-based learning models.
method Novel confidence intervals for Nyström method and sparse variational Gaussian process approximation.
result Improved performance bounds in regression and optimization problems.
Adaptive rule improves kernel-based gradient descent performance.
problem Improving convergence speed of kernel-based gradient descent algorithms.
method Empirical effective dimension for stopping rule, learning theory analysis, integral operator approach.
result Optimal learning rates and iteration bounds for KGD with adaptive stopping rule.
ConvNets can be translated into CKNs that perform similarly.
problem The distinction between ConvNets and kernel-based methods.
method Translation of ConvNets into CKNs using a new gradient algorithm.
result CKNs perform as well as ConvNets, supporting the translation.
A multi-layer KRR Auto-Encoder architecture for one-class classification.
problem One-class classification in machine learning.
method Multi-layer architecture of Kernel Ridge Regression Auto-Encoders with semi-supervised learning.
result Experimental results show the superiority of the proposed MKOC over existing one-class classifiers.
Optimizes learning rates for kernel-based expectile regression.
problem Estimating conditional expectiles efficiently.
method Support vector machine type approach using Gaussian RBF kernels.
result Learning rates are minimax optimal with a logarithmic factor.
Deep neural nets optimize kernel parameters for non-parametric two-sample tests.
problem Determining if two samples come from the same distribution.
method Deep kernels trained to maximize test power, adapting to distribution smoothness and shape.
result Deep kernels outperform simpler kernels in high dimensions and complex data.
New theoretical tools simplify kernel-based tests analysis.
problem Asymptotic behavior of kernel-based tests in various scenarios.
method Avoids complex expansions and limit theorems, works directly with Hilbert spaces random functionals.
result Framework leads to simpler analysis with minimal regularity conditions.
New algorithm improves kernel-based deep net performance.
problem Understanding deep learning and its mysteries.
method Exact computation of Convolutional NTK (CNTK) for infinite-width convolutional nets.
result CNTK results in 10% higher performance on CIFAR-10 than previous methods. Efficiently extracts features from large datasets using budgeted nonlinear subspace tracking.
problem Handling large-scale datasets with kernel-based methods while maintaining computational and memory efficiency.
method Low-rank, budgeted online subspace learning for feature extraction.
result Approximates high-dimensional features with a low-rank nonlinear subspace, leading to efficient kernel function approximation.
A new kernel-based CI test improves on existing methods.
problem Testing conditional independence (CI) in a broad range of dependencies.
method Regression-model-agnostic kernel-based CI test using reproducing kernel Hilbert spaces.
result GKCM outperforms state-of-the-art CI tests in simulations.
Laplace kernel feature selection offers statistical guarantees for nonparametric models with few samples.
problem Statistical guarantees for kernel-based feature selection in nonconvex optimization problems.
method Sharp characterization of the gradient of the objective function for Laplace kernel feature selection.
result Model-selection consistency for Laplace kernel-based feature selection in nonparametric settings with n∼logp samples. The paper analyzes SMOTE for imbalanced classification, providing theoretical bounds and guidelines.
problem The challenge of imbalanced classification problems, especially with minority classes.
method Theoretical analysis of SMOTE and related oversampling techniques for minority classes.
result Derives concentration and excess risk bounds for SMOTE and kernel-based classifiers.
Optimal kernel improves estimation accuracy in modal statistical methods.
problem Estimation accuracy of kernel-based modal statistical methods depends on the kernel used.
method The study theoretically shows an optimal kernel that minimizes asymptotic error criterion.
result An optimal kernel minimizes the error criterion when using an optimal bandwidth.
Develops an online nonparametric classifier for massive data.
problem Challenges of batch kernel-based nonparametric classifiers in massive data.
method Online principle components analysis to reduce dimensionality, followed by stochastic approximation algorithm for real-time calculation.
result Online classifier provides the best trade-off between accuracy and computation cost.
Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
problem Optimizing personalized objectives in a distributed system with limited global information.
method Kernel-based bandit framework with surrogate Gaussian process models, sparse approximations.
result Order-optimal regret performance (up to polylogarithmic factors) and reduced communication overhead.
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 …
DR-ABC uses kernel-based distribution regression for better ABC likelihood approximations.
problem Difficulties in exact posterior inference due to intractable likelihood functions.
method Kernel-based distribution regression for constructing better summary statistics.
result Superior performance compared to related methods on various problems.
Paper introduces new CMI estimators using classifiers and generative models.
problem Estimating conditional mutual information in high dimensions.
method Developed a classifier-based KL-Divergence estimator and used it to create CMI estimators.
result Proposed estimators perform better than existing methods, especially in high dimensions.
Quantum SVMs outperform classical ones on limited data.
problem Classifying and regressing with limited training data.
method Trained SVMs on D-Wave quantum annealer and compared to classical SVMs.
result Quantum SVMs often generalize better to unseen data.
FastKCI speeds up KCI tests for causal inference on large datasets.
problem Cubic computational complexity of kernel-based conditional independence tests.
method Mixture-of-experts approach with parallel Gaussian process inference.
result Substantial computational speedups with maintained statistical power.
Paper proposes an efficient causal discovery method with linear computational complexity.
problem Identifying causal relationships efficiently in large datasets.
method Approximate kernel-based generalized score function with low-rank technique and sampling algorithms.
result Significantly reduces computational costs while maintaining comparable accuracy.
New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.
problem Inaccurate error bounds for kernel-based system identification with unknown hyperparameters.
method Construct a high-probability set for true hyperparameters from marginal likelihood, then find worst-case posterior covariance.
result Proposed bounds contain true model with high probability and verified in simulations.
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 …
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…
Quantum machine learning for 2D classification tasks using optimized feature maps.
problem Classifying data points in finite feature space with quantum machine learning.
method Optimized quantum feature maps and classical model training.
result Exponentially better scaling of deployed kernels in qubit number.