New metrics needed for streaming ML due to delayed labels.
problem Streaming ML evaluation fails to identify unexpected performance.
method Recommend additional metrics for streaming ML performance.
result New metrics are needed for streaming ML due to delayed labels.
Efficiently identifies stable manifolds from streaming data.
problem Learning reliable manifolds from streaming data is computationally expensive.
method Presented error metrics and S-Isomap algorithm for efficient manifold learning.
result Identifies the transition point for stable manifold learning.
stream-learn is a Python library for analyzing data streams with various drift types.
problem Analyzing drifting and imbalanced data streams.
method Synthetic data stream generator, evaluation methodologies, and imbalanced binary classification metrics.
result Efficient implementation of classifiers for data stream analysis.
Study geometric properties of contactomorphism group with L 2 L^2 L 2 metric on stream functions.
problem Investigate geometric properties of contactomorphism group with L 2 L^2 L 2 metric. method Analyze sectional curvature and Riemannian exponential map of the contactomorphism group.
result Sectional curvature is always non-negative and Riemannian exponential map is not locally C 1 C^1 C 1 . Estimates bisimulation metrics from sample streams, not full transition models.
problem Estimating Markov chain metrics from limited sample data.
method Stochastic optimization using linear programming and primal-dual method.
result Validated through empirical evaluations, providing sample complexity guarantees.
Non-parametric method predicts multi-stream longitudinal data evolution.
problem Predicting the evolution of multi-stream longitudinal data for an in-service unit.
method Decomposes each stream into eigenfunctions and FPC scores, uses Gaussian process prior and empirical Bayesian updating.
result Framework outperforms state-of-the-art approaches and achieves high predictive accuracy.
This research tackles few-shot video action recognition, improving accuracy with a two-stream setup.
problem Few-shot video action recognition with limited labeled examples.
method Two-stream models combining convolutional and recurrent neural network video encoders with metric-based few-shot algorithms.
result The setup achieves 84.2% accuracy on a 5-shot 5-way task, outperforming other methods.
CURIE uses cellular automata to detect concept drift in data streams.
problem Detecting changes in data distribution (concept drift) in data streams.
method CURIE represents data stream distribution in a cellular automata grid and uses its neighborhood rule to detect changes.
result CURIE, when hybridized with base learners, performs competitively in detection metrics and classification accuracy.
A framework for evaluating and benchmarking concept drift detection methods
problem Data stream mining challenged by concept drift
method A novel benchmarking framework
result Fair comparisons of drift detection methods
Paper presents a reinforcement learning framework for personalized music playlist generation.
problem Misalignment between offline model objectives and online user satisfaction metrics in conventional playlist recommendation methods.
method Simulation-based reinforcement learning approach using a Deep Q-Network (DQN) modified to address large state and action spaces.
result The modified DQN (AH-DQN) policy leads to better user-satisfaction metrics compared to baseline methods during online A/B tests.
MaxSketch improves distinct counting in high-dimensional, noisy data streams.
problem Estimating distinct elements in high-dimensional, noisy data streams.
method MaxSketch uses random Gaussian projections to estimate distinct counts.
result MaxSketch achieves ( 1 + ε ) (1+\varepsilon) ( 1 + ε ) factor estimation with m = O ~ ( log n / ε 2 ) m = \widetilde{O} (\log n/\varepsilon^2) m = O ( log n / ε 2 ) random projections. Predicts user session length in streaming services with hierarchical modeling and shrinkage.
problem Predicting user session length in streaming services is challenging due to external factors and lack of covariates.
method Inspired by hierarchical Bayesian modeling, the approach incorporates flexible parametric/nonparametric models and uses hierarchical shrinkage.
result The method outperforms state-of-the-art estimators in efficiency and predictive performance.
A new method classifies multiple correlated data streams simultaneously.
problem Classifying multiple correlated data streams in practical scenarios.
method Double-Coupling Support Vector Machines (DC-SVM) considers both internal and external correlations.
result The proposed method outperforms traditional methods on artificial and real-world data streams.
A new method for estimating SW from streaming data.
problem Estimating Wasserstein distance from sample streams.
method Introducing a streaming estimator of the 1DW and applying it to all projections.
result Stream-SW achieves more accurate approximation of SW than random subsampling.
A new metric measures time series divergence efficiently.
problem Quantifying subtle differences in time series data.
method Sequence Likelihood (SL) divergence, a generalized KL divergence for time series.
result Efficient estimators of SL divergence from finite sample paths.
Data stream clustering tackles real-time data processing challenges.
problem Real-time processing of data streams with less prior information.
method Review of data stream clustering algorithms and their characteristics.
result Comparison and analysis of data stream clustering algorithms.
In this paper we construct the differential equations of the stream lines that characterize plasma regarded as a non-isotropic medium geometrized by a jet rheonomic time-invariant Berwald-Moor metric. Section 1 contains historical notes regarding the Plasma Physics and its geometrical description. Section 2 analyzes th…
New features capture the order of data streams.
problem Handling ordered moments in massive data streams.
method Introducing features for ordered moments.
result Theoretical guarantees for learning algorithms.
Bayesian tensor train method recovers streaming data with high accuracy.
problem Recovering high-order, incomplete, and noisy streaming data.
method Bayesian tensor train decomposition using streaming variational Bayes method.
result The proposed SPTT algorithm excels in recovering streaming data compared to state-of-the-art methods.
Scheme for online state discovery in financial markets using feature correlations and clustering.
problem Discovering temporal states in high-frequency financial data without human intervention.
method Unbiased Fourier estimator for feature correlations, high-speed clustering algorithm, state space enumeration.
result Feature cluster configuration is a candidate for system state representation.
A new framework detects novel classes in data streams.
problem Detecting novel class labels in data streams.
method Semi-supervised multi-task learning framework for co-representation learning.
result Superior performance over existing methods on real-world datasets.
Scikit-multiflow is a Python framework for multi-output/stream data mining.
problem Handling multi-output/stream data efficiently.
method Multi-output/multi-label stream data mining framework with state-of-the-art methods.
result Enables democratization of stream learning research.
New method rebalances evolving data streams incrementally.
problem Incremental rebalancing of evolving data streams.
method Proposes a new streaming approach for rebalancing data streams online.
result Outperforms existing approaches in rebalancing data streams.
PySAD offers a unified Python framework for efficient streaming anomaly detection.
problem Efficient anomaly detection in streaming data with strict constraints.
method Unified architecture with 17+ streaming algorithms, specialized components, and support for multiple learning paradigms.
result PySAD enables real-time processing with bounded memory and is compatible with other Python frameworks.
A new method detects anomalies in multivariate streams without unit dependence.
problem Detect anomalies in multivariate streams without unit dependence.
method Proposes SigMahaKNN combining variance norm and path signature.
result SigMahaKNN detects anomalies better than existing methods.
Efficiently identifies users from walking activity data using kernel-based DTW.
problem Identifying users from walking activity data streams.
method Learning a kernel to approximate DTW for efficient analysis of streaming data from wearable sensors.
result The proposed approach reduces computational burden compared to traditional DTW.
Context improves one-class classifiers in dynamic data streams.
problem Improving one-class classification in data streams with limited training data.
method Proposes using context to guide one-class classifier learning in data streams, presenting three frameworks.
result The use of context can improve the performance of streaming one-class classifiers.
Paper improves submodular streaming with better approximation, less memory, and lower complexity.
problem Maximizing submodular functions in streaming with a cardinality constraint.
method Sieve-Streaming++ with one pass, O ( k ) O(k) O ( k ) memory, and ( 1 / 2 ) (1/2) ( 1/2 ) -approximation; adaptive complexity reduction. result Achieves ( 1 / 2 ) (1/2) ( 1/2 ) -approximation with O ( k ) O(k) O ( k ) memory and low adaptive complexity. Sketches linear classifiers using Weight-Median Sketch for efficient data stream analysis.
problem Efficiently learning and analyzing data streams with limited memory.
method Introduces Weight-Median Sketch for compressed linear classifier learning over data streams.
result Memory-limited execution of various analyses over streams, including feature selection and mutual information estimation.
CROC identifies the earliest-changing stream as the root cause in multi-stream data.
problem Distribution-free root cause analysis in multi-stream data with unknown distributional changes.
method Conformal p-values and finite-sample valid confidence sets.
result CROC efficiently isolates the root cause under minimal assumptions.
Algorithm fine-tunes multi-view weights for streaming data.
problem Streaming data in multi-view learning.
method Fine-tuning combination weights of stable subspaces.
result Algorithm effectively handles streaming views.
Real-time prediction of SLA violations in telecom services.
problem Detecting and predicting SLA violations in dynamic telecom environments.
method Online machine learning approach using streaming device-level metrics.
result Highly accurate predictions (>90% classification accuracy and < 10% false alarm rate).
New method for recognizing human activities from streaming videos.
problem Scalable, incremental learning for human activity recognition.
method Incremental learning with active learning strategy.
result Outperforms existing methods in accuracy and model size.
Develops new Bayesian approach for analyzing streaming data.
problem Bayesian inference on streaming data is challenging.
method Population variational Bayes, approximating population posterior.
result Approximates population posterior for streaming data.
SDF adapts Deep Forest for evolving data streams with active learning.
problem Adapting Deep Forest for evolving data streams.
method Streaming Deep Forest (SDF) with Augmented Variable Uncertainty (AVU) active learning.
result SDF with AVU outperforms other methods trained with all instances by 70% labeling budget.
TSK-Streams learns fuzzy rules from data streams.
problem Adaptive learning from evolving data streams.
method Combines AMRules principles with fuzzy rule advantages.
result TSK-Streams performs highly competitively in experiments.
Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
problem Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
method Multi-Stream Fraud Transformer (MSFT) architecture that encodes each event stream with independent Transformer encoders and fuses their representations through configurable mechanisms.
result Sequence models significantly outperform gradient-boosted trees operating on aggregated features.
Dynamic Model Tree improves online learning for evolving data streams.
problem Effective and transparent machine learning on data streams is challenging.
method Revisit Model Trees for data stream applications, introducing Dynamic Model Tree.
result Dynamic Model Tree reduces the number of splits and outperforms state-of-the-art models.
LOFS library aids in online streaming feature selection.
problem Sequentially adding dimensions in high-dimensional data.
method State-of-the-art algorithms for online streaming feature selection.
result First open-source library for online streaming feature selection.
History PCA improves streaming PCA by retaining past data for better convergence.
problem Limited memory in small devices for high-dimensional data.
method History PCA algorithm that uses O ( B d ) O(Bd) O ( B d ) memory with B ≈ 10 B\approx 10 B ≈ 10 and O ( d ) O(d) O ( d ) memory with B ≈ 1 B\approx 1 B ≈ 1 . result History PCA converges faster and performs better than existing methods.
Improved flow matching using Gaussian processes for better sample quality.
problem Training continuous normalizing flows with reduced variance and flexibility.
method Extending conditional flow matching to streams modeled with Gaussian processes.
result Improved quality of generated samples with moderate computational cost.
Unified study of stateful replay for streaming learning, reducing forgetting by 2-3x.
problem Catastrophic forgetting in streaming generative and predictive learning.
method Unified analysis of stateful replay for autoencoding, forecasting, and classification tasks.
result Stateful replay reduces average forgetting by a factor of 2-3 on heterogeneous multi-task streams.
Low-precision streaming PCA estimates the leading eigenvector with limited precision.
problem Estimating the leading eigenvector in a streaming setting with limited precision.
method Oja's algorithm with linear and nonlinear stochastic quantization.
result A batched version of the quantized variants achieves the lower bound on quantization error up to logarithmic factors.
DSSCN improves lifelong learning of non-stationary data streams through adaptive network construction.
problem Lifelong learning of non-stationary data streams with efficient and adaptive models.
method Deep stacked stochastic configuration network (DSSCN) with self-constructing deep stacked network structure and adaptive hidden unit parameters.
result DSSCN outperforms existing data stream algorithms in continual learning of non-stationary data streams.
Paper detects changes in graph streams using CCMs and adversarial learning.
problem Detecting changes in stationarity in graph streams.
method Adversarial learning for graph embeddings on constant-curvature manifolds.
result The proposed methods outperform Euclidean embeddings in detecting even small changes.
New robustness certificates for streaming models with a sliding window.
problem Applying robustness certificates to streaming data with correlated inputs.
method Deriving robustness certificates for models using a sliding window over a sequence of potentially correlated inputs.
result Guarantees hold for the average model performance across the entire stream, independent of stream size.
Paper discovers process models from online event streams.
problem Discovering process models from continuous event streams.
method Generic architecture for process discovery in event streams.
result The proposed architecture enables process discovery from event streams.
Convolutional neural networks outperform other architectures in streaming time series classification.
problem Efficient deep learning models for real-time data streams.
method Asynchronous dual-pipeline deep learning framework for real-time predictions.
result Convolutional architectures achieve higher accuracy and efficiency in streaming time series classification.