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Found 13 papers, 3 papers with code
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems
Specifically, when the stationary distribution of the MDP belongs to an exponential family that is parametrized by policy parameters, we can improve existing policy gradient methods for average-reward RL.
Model-based Reinforcement Learning
Policy Gradient Methods
+1
Choosing the parameter of the Fermat distance: navigating geometry and noise
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given by Euclidean distances by exploding the geometrical and statistical properties of the dataset.
FEMDA: a unified framework for discriminant analysis
Although linear and quadratic discriminant analysis are widely recognized classical methods, they can encounter significant challenges when dealing with non-Gaussian distributions or contaminated datasets.
Symphony of experts: orchestration with adversarial insights in reinforcement learning
Structured reinforcement learning leverages policies with advantageous properties to reach better performance, particularly in scenarios where exploration poses challenges.
FEMDA: Une méthode de classification robuste et flexible
Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust.
Clustering for directed graphs using parametrized random walk diffusion kernels
In this paper, we introduce a new clustering framework, the Parametrized Random Walk Diffusion Kernel Clustering (P-RWDKC), which is suitable for handling both directed and undirected graphs.
Generalized Spectral Clustering for Directed and Undirected Graphs
In this paper, we present a generalized spectral clustering framework that can address both directed and undirected graphs.
Robust classification with flexible discriminant analysis in heterogeneous data
Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust.
Clustering high dimensional meteorological scenarios: results and performance index
The Reseau de Transport d'Electricit\'e (RTE) is the French main electricity network operational manager and dedicates large number of resources and efforts towards understanding climate time series data.
Improving the performance of heterogeneous data centers through redundancy
As such, our result is the first in showing that redundancy can improve the stability and hence performance of a system when copies are non-i. i. d..
Networking and Internet Architecture Probability
Uncovering differential equations from data with hidden variables
SINDy is a method for learning system of differential equations from data by solving a sparse linear regression optimization problem [Brunton et al., 2016].
A flexible EM-like clustering algorithm for noisy data
Though very popular, it is well known that the EM for GMM algorithm suffers from non-Gaussian distribution shapes, outliers and high-dimensionality.
Nonhomogeneous Euclidean first-passage percolation and distance learning
Consider an i. i. d.
Probability 60D05, 62G99
