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Found 11 papers, 1 papers with code
A Risk Minimization Principle for a Class of Parzen Estimators
This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms.
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.
Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization
We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set.
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning
Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications.
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings
Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort.
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Ranked #1 on
Sleep Stage Detection
on SHHS
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value
In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data.
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions
Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems
This is altered by the modifications reported in this brief that drastically improve the computational characteristics.
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints
In this work we try to unify these existing techniques and bridge the gap with classical optimization and control theory, using a generic primal-dual framework for value-based and actor-critic reinforcement learning methods.
