Unified approach to localized kernel learning improves performance.
problem Learning the best kernel for each training example.
method Unified system and new algorithm design.
result Improved performance through localized kernel learning.
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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.
169,341 papers · 148 categories
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48 results for “localized kernel learning”
New kernels capture both local and non-local interactions efficiently.
problem Designing kernels that capture both local and non-local interactions while remaining computationally tractable.
method Spectral truncation kernels based on -algebra.
result Spectral truncation kernels induce interactions across the data function domain and reduce computational cost.
New classifier combines locally linear kernels for fast and accurate non-linear classification.
problem Developing a fast and accurate non-linear classifier.
method Combines locally linear classifiers using a Multiple Kernel Learning (MKL) problem with scalable MKL training for streaming kernels.
result The resulting classifier achieves high accuracy with fast inference time.
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.
Generalizes machine learning models using localization kernels and local means.
problem Understanding and unifying diverse machine learning models.
method Formal definition of localization method through localization kernels and local means.
result Unified theoretical lens and new methodological tools for designing flexible learning systems.
Paper analyzes localized SVMs for robustness and consistency.
problem Handling large datasets efficiently and robustly.
method Localized support vector machines (SVMs) for non-parametric learning.
result Locally learnt kernel methods are universal consistent and robust.
A new convolutional spectral kernel network learns hierarchical and local features.
problem Lack of deep learning in non-stationary spectral kernels.
method Introduces convolutional filters and deep architectures into non-stationary spectral kernels, derives generalization error bounds, and introduces regularizers.
result Validated the effectiveness of the convolutional spectral kernel network on real-world datasets.
Proposes a method for deriving local group invariant representations using kernel methods.
problem Invariance to nuisance transformations in group settings.
method Kernel methods and probability distributions over the group to induce distributions in the input feature space.
result Uniform convergence bounds and excess risk bounds for learning with local invariant random feature maps.
Unified framework combines trace-induced quantum kernels for improved machine learning models.
problem Improving performance of quantum machine learning models using trace-induced kernels.
method Developed a unified framework combining various trace-induced quantum kernels, including global fidelity and local projected kernels, as Lego kernels.
result Local projected kernels can achieve comparable performance to global fidelity kernels with fewer quantum resources.
New quantum kernels avoid overfitting by combining local and global components.
problem Exponential concentration in quantum kernels leads to overfitting.
method Local-global quantum kernels combining small subsystem and full-system measurements.
result Demonstrated benign overfitting in local-global quantum kernels.
LMKL-Net uses deep neural networks to solve localized multiple kernel learning faster and more efficiently.
problem Localized multiple kernel learning (LMKL) optimization problem.
method LMKL-Net employs a feedforward deep neural network with attentional networks and multilayer perceptrons to learn kernel combination weights and multiclass classifiers.
result LMKL-Net outperforms state-of-the-art MKL solvers in accuracy and is trained much faster and with less memory.
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. T…
Flexible Kernels for Protein Property Prediction
problem Predicting protein properties from sparse experimental data
method Sequence kernels using evolutionary substitution matrices and local linearity
result Data-efficient models of protein property landscapes
We derive an upper bound on the local Rademacher complexity of -norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches aimed at analyzed the case only while our analysis covers all cases , assuming the different feature …
Locality helps in learning from high-dimensional data.
problem Understanding how convolutional neural networks learn from high-dimensional data.
method Teacher-student framework for kernel regression with convolutional kernels.
result Locality is key to determining the learning curve exponent in high-dimensional data.
COKE reduces communication in decentralized kernel learning.
problem Decentralized kernel learning challenges due to data-dependent decision variables.
method Random feature approximation and ADMM for iterative optimization; communication-censored algorithm.
result COKE achieves faster convergence and reduced communication load.
HKConv learns hyperbolic features by aggregating kernel points.
problem Challenges in learning good hyperbolic representations using Euclidean operations.
method Proposes HKConv, a trainable hyperbolic convolution that correlates local features with kernel points and aggregates them.
result HKConv learns expressive local features according to hyperbolic geometry and enjoys equivariance to permutation and invariance to parallel transport.
Proposes DR-ME test for interpretable distributional treatment effects.
problem Detects invisible differences in treatment effects on distributional outcomes.
method Semiparametrically efficient finite-location test using kernel witnesses and orthogonal features.
result DR-ME reveals causal-discrepancy coordinates and has noncentral chi-square local power.
The paper proves deep ReLU networks avoid spurious local minima in NTK regime.
problem The existence of spurious local minima in deep ReLU neural networks.
method Theoretical proof under Neural Tangent Kernel regime.
result Deep ReLU networks do not lie in spurious local minima in NTK regime.
Agents learn locally, converge globally in online learning with kernels.
problem Multi-agent learning with limited data and communication.
method Local regression functions with consensus constraints, functional stochastic gradient descent, and greedy subspace projections.
result Agents' functions converge to a neighborhood of the globally optimal one as the penalty parameter increases.
Enhanced kernel ridgeless regression improves performance with LAB RBF kernels.
problem Lack of flexibility in kernel ridgeless regression.
method Locally-Adaptive-Bandwidths (LAB) RBF kernels and kernel learning techniques.
result Functions learned from LAB RBF kernels belong to an integral space of RKHSs, demonstrating robust generalization.
Paper develops sparse learning for heavy-tailed time series with locally stationary dynamics.
problem Sparse learning for high-dimensional heavy-tailed locally stationary time series.
method Additive modeling with kernel smoothing, sparsity-inducing penalized estimation.
result Prediction-error bounds and convergence rates for different sparsity structures.
Enhances nearest neighbor classifier performance with local distance metric learning.
problem Inconsistent data distribution across feature space.
method Local Mahalanobis Distance Learning (LMDL) considers neighborhood influence and learns multiple distance metrics for prototypes.
result LMDL improves nearest neighbor classifier performance on various datasets.
Adapts manifold structure for better clustering performance.
problem Lack of consideration for local manifold structure in existing multiple kernel k-means methods.
method Adopts manifold adaptive kernel to integrate local manifold structure of kernels.
result Proposed method outperforms state-of-the-art methods.
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.
New recursive algorithm estimates conditional kernel mean embeddings in Hilbert space.
problem Estimating conditional distributions in RKHS for supervised learning.
method Recursive algorithm in space for conditional kernel mean map.
result Strong consistency of recursive estimator proved.
Deep Kernel Learningcs.LG
We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and struc…
Estimates path-valued data using signature metrics and local kernels.
problem Nonparametric regression and classification for path-valued data.
method Combines signature transform and local kernel regression.
result Establishes convergence bounds and demonstrates competitive accuracy.
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systemsstat.ML
Scalable Gaussian Process Operator tackles high-dimensional PDEs.
problem Scaling Gaussian Process Operators to high-dimensional, data-intensive regimes.
method Nearest-neighbor-based local kernel approximations, sparse kernel approximation, structured Kronecker factorizations, operator-aware kernel structures, task-informed mean functions.
result Consistently achieves high accuracy across varying discretization scales.
Stochastic variational deep kernel learning improves classification performance.
problem Combining deep learning and kernel methods for improved classification.
method Proposes a novel deep kernel learning model with stochastic variational inference.
result Shows improved performance on various benchmarks, including large datasets.
Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testingstat.ME
Unified framework for global and local two-sample conditional distribution testing.
problem Testing equality of two conditional distributions.
method Distance and kernel methods, conditional U-statistics, local bootstrap.
result Developed reliable global and local tests.
On Local Heat Kernelmath-ph
The paper studies local heat kernel properties on smooth manifolds.
problem Understanding heat kernel properties in open convex sets of smooth Riemannian manifolds.
method Utilizes path integral formulation to investigate properties like uniqueness, symmetry, and asymptotics.
result Uniqueness and symmetry of Seeley-DeWitt coefficients are established.
Proposes a continuous, differentiable model from local adaptive models.
problem Inadequate continuity and differentiability in over-parameterized models.
method A global continuous and differentiable model constructed from weighted averages of locally learned models.
result Achieves faster statistical convergence and improved performance in various settings.
Machine learning and geostatistics are powerful mathematical frameworks for modeling spatial data. Both approaches, however, suffer from poor scaling of the required computational resources for large data applications. We present the Stochastic Local Interaction (SLI) model, which employs a local representation to impr…
Study local convergence of GDA for training GANs with kernel-based discriminators.
problem Analyzing the local dynamics of GDA for GANs with kernel-based discriminators.
method Linearization of a non-linear dynamical system, under an isolated points model assumption.
result Showed phase transitions indicating convergence, oscillation, or divergence of GDA.
We consider learning on graphs, guided by kernels that encode similarity between vertices. Our focus is on random walk kernels, the analogues of squared exponential kernels in Euclidean spaces. We show that on large, locally treelike, graphs these have some counter-intuitive properties, specifically in the limit of lar…
Unified analysis of kernel-based and locally adaptive bandit optimization methods.
problem Performance of bandit optimization algorithms in RKHS functions.
method Investigates the relationship between kernel regularity and algorithmic performance, characterizing spectral properties of various kernels.
result Unified framework for analyzing kernel-based and locally adaptive bandit algorithms, deriving explicit regret bounds.
Kernel clustering methods have biases due to density, which can be corrected.
problem Density biases in kernel clustering methods.
method Theoretical analysis and proposed solutions to density biases.
result Density biases can be corrected by density equalization using locally adaptive weights or kernels.
Improved SVMs handle large datasets more efficiently and robustly.
problem Handling large datasets in SVMs for runtime and storage.
method Developed a locally learned predictor using influence function analysis.
result The locally learned predictor is differentiable and robust to distribution changes.
DSoftKI scales GP regression with full derivative observations.
problem Efficiently fitting and predicting full derivative observations in Gaussian Processes.
method Extends SoftKI by using local temperature vectors for interpolation, enabling encoding of local directional sensitivity.
result DSoftKI achieves accurate predictions and scales to larger datasets with full derivative observations.
Proposes a new graph kernel framework using regularized Wasserstein distances.
problem Learning optimal transport distances for graph kernels.
method Introduces Regularized Wasserstein (RW) discrepancy with two regularization terms.
result Empirically validated method outperforms state-of-the-art methods.
Kernel embeddings map measures to functions in RKHS, addressing embedding and metric properties.
problem Characterizing sets of measures that can be embedded and the conditions for embedding to be injective.
method Study of kernel mean embeddings, focusing on universal, characteristic, and strictly positive definite kernels.
result Unified and extended results on embedding and metric properties of measures.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approachcs.LG
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of…
Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.
problem Loss of locality in high-dimensional Gaussian process regression.
method Analysis of Matern family kernels and multi-zeta basis functions.
result The property of locality disappears in high dimensions, impacting regression quality.
This paper improves neural tangent kernels for better generalization and local elasticity.
problem Performance gap between neural tangent kernels and real-world neural networks.
method Introduces label-aware kernels using Hoeffding decomposition.
result Models trained with proposed kernels simulate NNs better in terms of generalization and local elasticity.
KSOS improves kernel learning for dynamical systems via global optimization.
problem Challenges in selecting optimal kernels and tuning parameters in traditional kernel-based methods.
method Global optimization framework with kernel-based surrogate functions.
result KSOS consistently outperforms gradient descent in predicting dynamical systems.
Improved kernel ridge regression for large datasets using weighted random binning.
problem Efficiently approximating kernel matrices for large-scale datasets.
method Introduced weighted random binning features for locality sensitive hashing.
result Weighted random binning features generate Gaussian processes of any desired smoothness.
A new algorithm for decentralized learning in heterogeneous networks reduces sub-optimality over time.
problem Learning in decentralized heterogeneous networks with local data streams and nonlinear constraints.
method Functional variant of stochastic primal-dual method with greedy subspace projection.
result The HALK algorithm achieves sub-optimality reduction and constraint satisfaction.