The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are ob…
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48 results for “assignment kernels”
New kernel learns optimal bijection for graph classification.
problem Graph classification with optimal bijection.
method Multiple kernel learning for Weisfeiler-Lehman assignment kernels.
result Feasibility and effectiveness of the approach demonstrated.
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classificationstat.ML
Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the fail…
The paper studies invariant weighted Bergman metrics on domains.
problem Investigating invariant weighted Bergman metrics under biholomorphisms.
method Introducing invariant weight assignments, using Bergman's minimum integral method and domain version of Tian-Yau-Zelditch expansion.
result Uniform convergence of weighted Bergman kernels and metrics on uniform squeezing domains.
Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of model parameters, which is substantially easie…
DHGAK aligns substructures for better graph kernel performance.
problem Limited performance of traditional graph kernels due to missing substructure similarities.
method Hierarchically aligns relational substructures in deep embedding space, assigning same feature maps in RKHS.
result DHGAK outperforms state-of-the-art graph kernels on various benchmarks.
In this work, we propose the kernel Pitman-Yor process (KPYP) for nonparametric clustering of data with general spatial or temporal interdependencies. The KPYP is constructed by first introducing an infinite sequence of random locations. Then, based on the stick-breaking construction of the Pitman-Yor process, we defin…
We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of output units, given t…
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problemstat.ML
A new algorithm improves solving QAP with better performance.
problem Solving the Quadratic Assignment Problem (QAP) efficiently.
method Estimation of Distribution Algorithms (EDAs) with a non-parametric distance-based Mallows model.
result The proposed algorithm outperforms existing methods for QAP.
A new method for analyzing adaptive experiments using kernel treatment effects.
problem Efficiently analyzing adaptive experiments that adjust treatment assignments based on outcomes.
method Kernel Treatment Effects (KTE) framework combining RKHS scores and witness functions.
result Effective for both mean shifts and higher-moment differences, outperforming adaptive baselines.
New method uses multiple kernels to improve SVGD performance.
problem Sub-optimal performance of single kernel in SVGD.
method Combines multiple kernels to approximate optimal kernel, using Kernelized Stein Discrepancy (KSD) and constructing Multiple Kernel SVGD (MK-SVGD).
result Consistently matches or outperforms competing methods in experiments.
Improved text classification performance through conformal transformations of kernels.
problem Text document categorization in high-dimensional spaces.
method Introduced new Gaussian Cosine kernel and two conformal transformations.
result Conformal transformations significantly improve kernel performance, especially for sub-optimal kernels.
We investigate the short-time expansion of the heat kernel of a Laplace type operator on a compact Riemannian manifold and show that the lowest order term of this expansion is given by the Fredholm determinant of the Hessian of the energy functional on a space of finite energy paths. This is the asymptotic behavior to …
A method to improve LLMs by automating the construction of a mixture of expert prompts.
problem Limitation of single instruction prompts in covering complex problem spaces.
method Divide the problem space into sub-regions, each governed by a specialized expert with both an instruction and demos. A two-phase process constructs these experts.
result Achieves an average win rate of 81% across major benchmarks.
Unified approach to graph matching using Hilbert spaces and entropy-regularized Frank-Wolfe algorithm.
problem Graph matching problems in computer vision, pattern recognition, and bioinformatics.
method Unified view of Koopmans-Beckmann's QAP and Lawler's QAP, introducing new rules for array operations in Hilbert spaces. Entropy-regularized Frank-Wolfe (EnFW) algorithm for QAP optimization.
result Our approach significantly outperforms state-of-the-art in matching accuracy and scalability.
Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic mul…
We attempt to set a mathematical foundation of immunology and amino acid chains. To measure the similarities of these chains, a kernel on strings is defined using only the sequence of the chains and a good amino acid substitution matrix (e.g. BLOSUM62). The kernel is used in learning machines to predict binding affinit…
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clusteringcs.LG
MIK improves t-SNE's local structure preservation in biological sequence data.
problem Efficiently preserving local structure in high-dimensional biological sequence data.
method Modified Isolation Kernel (MIK) using adaptive density estimation.
result MIK preserves local and global structure better than Gaussian and isolation kernels.
New method for causal inference with complex treatment compositions.
problem Estimating causal effects with compositional treatments.
method Kernel-based covariate functional balancing approach.
result Achieves -consistency without requiring consistent estimation of weights.
We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. Then we apply kernel density estimation and mode clustering to the selected features. The output of the method consists of a list of relevant f…
Kernel -means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear -means clustering algorithm. However, kernel -means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel …
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Study improves multi-class domain generalization with a new error bound.
problem Improving multi-class classification performance across multiple domains.
method Kernel-based learning algorithm with a logarithmic generalization error bound.
result Achieved significant performance gains over a pooling strategy empirically.
This work tackles scalable sampling for nonsymmetric DPPs.
problem Scalability issue in existing DPP sampling algorithms for nonsymmetric DPPs.
method Developed a linear-time algorithm for kernels with low-rank structure and a sublinear-time rejection sampling algorithm.
result Bounded rejection rate for kernels with structural constraints.
A new method for density estimation using nearest neighbor Dirichlet mixtures.
problem Slow and unstable Bayesian density estimation methods.
method Nearest neighbor grouping, local Bayesian parametric models, Dirichlet prior, Monte Carlo sampling.
result Effective density estimation with improved computational efficiency.
Meta-learning framework uses task similarity through nonparametric kernel regression.
problem Limited tasks and outliers/dissimilar tasks hinder meta-learning performance.
method Nonparametric kernel regression to quantify and use task similarity.
result Meta-learning algorithm outperforms existing methods in task-limited settings.
Efficiently combines probabilistic predictions using kernel embeddings.
problem Combining multiple probabilistic predictions to improve forecast accuracy.
method Embed predictions into RKHS, optimizes kernel-based scoring rules, finds efficient implementation.
result Generalised linear pool outperforms traditional linear pool in operational wind speed forecasts.
A new weighted FDA method improves face recognition accuracy.
problem Equal treatment of all class pairs in FDA leads to suboptimal performance.
method Cosine-weighted and automatically weighted FDA methods are proposed.
result Improved face recognition accuracy through weighted FDA.
Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel processstat.AP
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
Real time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve this problem. However, pruning usually results in irregular network connections that not only demand extra representation efforts but also …
Framework for joint inference of network topology and interaction types in heterogeneous systems.
problem Joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems.
method Three-stage approach: shared structure recovery, discrete interaction type identification, and matrix factorization.
result The method yields accurate reconstruction of underlying dynamics and is robust to noise.
New method learns low-dimensional representations of AI-generated treatments.
problem Representing AI-generated treatments without losing semantic meaning.
method Double kernel representation learning with alternating minimization.
result Efficiently learned representations guide generative models and facilitate adaptive online experiments.
When a gauge-natural invariant variational principle is assigned, to determine {\em canonical} covariant conservation laws, the vertical part of gauge-natural lifts of infinitesimal principal automorphisms -- defining infinitesimal variations of sections of gauge-natural bundles -- must satisfy generalized Jacobi equat…
Method estimates treatment effects with continuous values, correcting for confounding.
problem Estimating treatment effects with continuous values, dealing with confounding.
method Two-stage kernel ridge regression: first stage learns response, second stage corrects for distribution shift.
result Optimal learning bounds achieved without estimating treatment density, adapts to unknown overlap and kernel spectral decay.
Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al (2013) used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable.…
New method estimates hazard ratios without bias in observational studies.
problem Uninterpretable hazard ratios due to unspecified baseline hazard.
method Kernel-based machine learning to model risk set changes.
result Debiased maximum-likelihood estimators identify true hazard ratios.
Bayesian optimization method for permutations accelerates combinatorial search.
problem Optimizing expensive-to-evaluate objectives on permutation problems.
method LAW2ORDER, a batch Bayesian optimization method based on the acquisition weighted kernel.
result LAW2ORDER achieves sublinear batch cumulative regret, demonstrating accelerated search.
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenc…
Estimates individual treatment effects using gradient interpolation and kernel smoothing.
problem Estimating individualized continuous treatment effects in observational data.
method Augment training data with independently sampled treatments and inferred counterfactual outcomes using gradient interpolation and kernel smoothing.
result Our method outperforms state-of-the-art methods on counterfactual estimation error.
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD based K-chart was first introduced by Sun and Tsung for monitoring multivariate processes when underlying distribution of process parameters or quality characteristics depart from Norm…
Unified view on selective credit assignment for reinforcement learning.
problem Efficient credit assignment in reinforcement learning.
method Unified temporal-difference algorithms with selective weightings.
result New algorithms for backward credit assignment and off-policy learning.
Proposes a method to estimate personalized treatments from high-dimensional data.
problem Estimating individualized treatment regimes (ITRs) from high-dimensional covariates.
method Directly targets the contrast between potential outcomes, using dimension-reduced outcome-weighted learning.
result Achieves universal consistency, converging to the Bayes risk under mild conditions.
New scalable MCMC sampling for nonsymmetric DPPs speeds up computations.
problem Efficient sampling for nonsymmetric DPPs with low-rank kernels.
method Enhanced rejection sampling with efficient proposal distribution construction.
result Sublinear runtime for scalable MCMC sampling of -NDPPs.
GRAMPA spectral method solves graph matching problem with high probability.
problem Finding vertex correspondence between unlabeled graphs.
method GRAMPA constructs a similarity matrix from weighted eigenvector comparisons, rounding to produce a matching.
result GRAMPA exactly recovers correct vertex correspondence with high probability for Gaussian models.
NOs can learn any finite collection of classes in functional data.
problem Learning finite collections of classes in infinite-dimensional spaces.
method Proved sample-based neural operators can learn any finite collection of classes in an infinite-dimensional reproducing kernel Hilbert space.
result NOs can learn any finite collection of classes in an infinite-dimensional reproducing kernel Hilbert space, even when the classes are not convex or connected.
GP model for time series forecasting with priors.
problem Automatic selection of optimal kernels and reliable estimation of hyperparameters.
method Fixed composition of kernels, automatic relevance determination (ARD), empirical Bayes priors.
result GP model is more accurate than state-of-the-art models.
New algorithms assign credit to past decisions based on hindsight.
problem Efficient credit assignment in reinforcement learning.
method Explicit credit assignment using new data in hindsight.
result Value functions can be rewritten to include hindsight credit assignment.
A new model simulates non-linear adsorption using Gaussian KDEs.
problem Simulating non-linear adsorption processes in porous materials.
method Combines random walk particle tracking with Gaussian Kernel Density Estimators for nonlinear modeling.
result Effective reproduction of Langmuir and Freundlich isotherms.