arXiv research

A method for learning rankings in non-stationary data streams.

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of rand…

In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To spe…

Bayesian non-parametric model adapts to concept drifts in streaming data.

This research generates synthetic data streams for handling concept drifts and novel classes.

SONAR improves outlier detection for streaming data with strong theoretical guarantees.

Large scale, streaming datasets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training and robust to the presence of non-stationarity. In this work consider the problem of learning $\ell_1$ regularized linear models in the context of streaming data. In particu…

TSK-Streams learns fuzzy rules from data streams.

Improves decision tree performance by correcting split selection errors.

This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical…

Efficiently learns neural network parameters from streaming data.

Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no t…

LUNAR uses cellular automata for real-time data classification in fast streams.

Efficiently trains GMMs for streaming data with SGD, addressing local optima and numerical instabilities.

While deep learning has achieved remarkable results on various applications, it is usually data hungry and struggles to learn over non-stationary data stream. To solve these two limits, the deep learning model should not only be able to learn from a few of data, but also incrementally learn new concepts from data strea…

OML-AD detects anomalies in non-stationary time series data.

Unified approach for learning state representations from streaming data.

Paper addresses challenges in benchmarking stream learning algorithms with real-world data.

New Hermite series estimator for Spearman rank correlation in non-stationary data.

This paper studies concept drift detectors for financial time series.

DDG-DA predicts future data distribution to adapt models for predictable concept drift.

Framework models supervised learning in non-stationary data.

HHAR-net uses neural networks to recognize human activities at different levels of abstraction.

Signatures simplify analysis of evolving data streams.

We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data.We consider standard winner-takes-all updates known as LVQ1. Statistical prope…

Prototype-based generative replay framework for online continual regression.

Study online conformal prediction for non-stationary data with optimal training-conditional regret.

The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which can…

DriftMoE adapts to concept drifts in data streams efficiently.

New method tackles dynamic data labeling issues with limited labels.

STVNN models spatiotemporal data using covariance matrices.

We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the network architecture of RBM according to the statistics of streaming data. The OGD-RBM is trained in…

Hop Sampling improves GNNs in non-stationary environments by preventing overfitting.

This thesis tackles continual learning in neural networks, overcoming forgetting.

Develops RL algorithm for non-Markovian, non-stationary reward streams.

Continual learning is hard for AI, leading to forgetting old knowledge.

This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust $l^1$-norm cost function in order to estimate and track non-stationary subspaces when the streaming data …

Study improves neural network performance in sequential learning for image classification.

We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with $β$-divergences. The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a const…

We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we …

New policy for non-stationary multi-armed bandits with recovering rewards.

Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…

Paper addresses private online convex optimization with optimal algorithms in various geometries and high-dimensional bandits.

Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.

Online distributional prediction with latent cluster geometry

Data stream clustering tackles real-time data processing challenges.

Bayesian tensor train method recovers streaming data with high accuracy.

A new fuzzy time series method for non-stationary data.