Search Results for author:
Found 30 papers, 6 papers with code
Online Learning of Decision Trees with Thompson Sampling
Recent breakthroughs addressed this suboptimality issue in the batch setting, but no such work has considered the online setting with data arriving in a stream.
A Historical Context for Data Streams
Machine learning from data streams is an active and growing research area.
An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
Yaw misalignment, measured as the difference between the wind direction and the nacelle position of a wind turbine, has consequences on the power output, the safety and the lifetime of the turbine and its wind park as a whole.
Shapley Chains: Extending Shapley Values to Classifier Chains
Compared to existing methods, this approach allows to attribute a more complete feature contribution to the predictions of multi-output classification tasks.
Transferable Deep Metric Learning for Clustering
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality.
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
In this paper, we consider missing value imputation as a multi-label classification problem and propose Chains of Autoreplicative Random Forests.
Learning from Data Streams: An Overview and Update
We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks.
Estimating Multi-label Accuracy using Labelset Distributions
In this article we estimate the expected accuracy as a surrogate for confidence, for a given accuracy metric.
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
Conv-NILM-net is a causal model for multi appliance source separation.
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach
In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled.
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task.
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
Isomorphic Cross-lingual Embeddings for Low-Resource Languages
Following this, we use joint training methods to develops CLWEs for the related language and the target embed-ding space.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Optimality in Noisy Importance Sampling
In this work, we analyze the noisy importance sampling (IS), i. e., IS working with noisy evaluations of the target density.
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare.
River: machine learning for streaming data in Python
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering.
Better Sign Language Translation with STMC-Transformer
This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language.
Ranked #1 on
Sign Language Translation
on ASLG-PC12
(using extra training data)
Classifier Chains: A Review and Perspectives
This performance led to further studies of how exactly it works, and how it could be improved, and in the recent decade numerous studies have explored classifier chains mechanisms on a theoretical level, and many improvements have been made to the training and inference procedures, such that this method remains among the state-of-the-art options for multi-label learning.
Probabilistic Regressor Chains with Monte Carlo Methods
A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features.
Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation
A major focus in the data stream literature is on designing methods that can deal with concept drift, a challenge where the generating distribution changes over time.
Perturb and Combine to Identify Influential Spreaders in Real-World Networks
Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure.
Scikit-Multiflow: A Multi-output Streaming Framework
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language.
Multi-label Methods for Prediction with Sequential Data
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.
Data Stream Classification using Random Feature Functions and Novel Method Combinations
Big Data streams are being generated in a faster, bigger, and more commonplace.
Multi-label Classification using Labels as Hidden Nodes
We extend some recent discussion in the literature and provide a deeper analysis, namely, developing the view that label dependence is often introduced by an inadequate base classifier, rather than being inherent to the data or underlying concept; showing how even an exhaustive analysis of label dependence may not lead to an optimal classification structure.
Deep Learning for Multi-label Classification
In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time.
Kaggle LSHTC4 Winning Solution
The number of documents per label is chosen using label priors and thresholding of vote scores.
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems.
