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.
Kernel-based function approximation improves reinforcement learning performance.
KeRNS tackles non-stationary reinforcement learning in metric spaces.
Kernel-UCBVI algorithm balances exploration and exploitation in metric state-action spaces.
Kernel-based reinforcement learning (KBRL) stands out among reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kernel approximation, KBRL provides a way of computing a decision policy which is statistically consistent and converges to a unique solution. U…
Study preference-based reinforcement learning in episodic kernel MDPs.
Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.
The paper improves QD policy ensembles using distribution ratio estimators.
New RL method reduces sample complexity for large state-action spaces.
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework …
Paper shows robustness of kernel-based pairwise learning without strict assumptions.
Research aims to improve confidence intervals for RKHS elements in online learning.
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 …
Neural-Kernel CME tackles scalability and expressiveness challenges in conditional distribution representation.
Novel confidence intervals improve convergence rates for sparse kernel-based models.
Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
Kernel-based L2-boosting with structure constraints improves regression efficiency.
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…
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity. Traditional measures such as confidence intervals may be insufficient…
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 …
This paper improves kernel-based regression using transfer learning.
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 …
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…
Paper proposes adaptive parameter selection for KGD algorithms.
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness me…
We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed o…
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
GP+ is a Python library for Gaussian process learning.
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, (2) approximate the estimator using a few variables by l1-type penalized estimation. We see that the…
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that generalizes a recent approach called "kernel-based dynamic mode d…
Paper analyzes how contrastive learning structures learned representations.
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality. This paper builds the mathematical framework to approximate cooperative MARL by a mean-field control (MFC) approach, and shows that the approximation error is of $\mathcal{O}(\frac{1}{\sqrt…
Kernel-based tests for shape constraints in finance.
Meta two-sample testing uses auxiliary data to quickly find powerful tests from limited samples.
KSOS improves kernel learning for dynamical systems via global optimization.
ABC learns context-aware representations for clustering.
Study provides guarantees for kernel clustering under non-parametric mixtures.
Method approximates high-dimensional feature vectors for supervised learning.
Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are u…
We present a novel machine learning approach to understanding conformation dynamics of biomolecules. The approach combines kernel-based techniques that are popular in the machine learning community with transfer operator theory for analyzing dynamical systems in order to identify conformation dynamics based on molecula…
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "kernel mean embeddings", are leading methods for two-sample and independence tests from the machine learning community. A fixed-point transf…
Paper introduces EO_k for quantifying accuracy-fairness trade-offs in FRL.
New method estimates hazard ratios without bias in observational studies.
New theoretical tools simplify kernel-based tests analysis.
New method speeds up kernel-based machine learning for force field reconstruction.
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of iterations in KGD and derive an implementable early stopping strategy. We analyze the performance of the adaptive stopping rule in the fram…
A new kernel-based CI test improves on existing methods.
Laplace kernel feature selection offers statistical guarantees for nonparametric models with few samples.