Links clusters streaming data vectors in high dimensions.
problem Efficient clustering of streaming high-dimensional data.
method Online clustering algorithm for unit vectors.
result Successfully applied to face and voice recognition.
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48 results for “link streams”
This paper reviews methods for distributed training of machine learning models from high-rate streams.
problem Training machine learning models from high-rate distributed streams in a compute- and bandwidth-limited setting.
method Recently developed methods for large-scale distributed stochastic optimization.
result There exist regimes where systems can learn from distributed, streaming data at order-optimal rates.
Paper introduces new graph concepts for better modeling of temporal interactions.
problem Graph theory struggles to capture temporal and structural aspects of interactions.
method Generalizes graph concepts to handle both temporal and structural aspects of interactions.
result Formalism allows direct modeling of interactions over time, similar to graph theory.
Derives FPDE for equity-linked insurance pricing.
problem Calculating prices for insurance policies with complex payment histories.
method Variational techniques in functional Itô calculus.
result Derives a functional partial differential equation.
The paper analyzes time-dependent streaming data with biased gradient estimates and proposes improved stochastic optimization methods.
problem Stochastic optimization in a streaming setting with time-dependent and biased gradient estimates.
method Analysis of several first-order methods including SGD, mini-batch SGD, and time-varying mini-batch SGD, along with their Polyak-Ruppert averages.
result Time-varying mini-batch SGD methods can break long- and short-range dependence structures, and biased SGD methods can achieve comparable performance to their unbiased counterparts.
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.
Pangloss improves entity linking in noisy text environments.
problem Entity linking in non-grammatical, loosely-structured text.
method Combines probabilistic key phrase identification and semantic similarity engine.
result Better than state-of-the-art results (>5% in F1).
M-FISHER detects and adapts to streaming data shifts with statistical validity and stability.
problem Detecting and adapting to distributional shifts in streaming data.
method Constructs an exponential martingale from non-conformity scores and applies Ville's inequality for detection. Fisher-preconditioned updates for adaptation.
result Establishes M-FISHER as a principled approach for robust, anytime-valid detection and geometrically stable adaptation.
SA-ABR uses UAV sensor data to optimize video streaming quality.
problem Dynamic UAV flight states cause fluctuating video streaming quality.
method SA-ABR integrates sensor data with network observations to train a DRL model.
result SA-ABR outperforms existing ABR algorithms by 21.4% in QoE.
Motivated by machine learning applications in networks of sensors, internet-of-things (IoT) devices, and autonomous agents, we propose techniques for distributed stochastic convex learning from high-rate data streams. The setup involves a network of nodes---each one of which has a stream of data arriving at a constant …
A new model VCM improves collaborative filtering by synchronously linking two VAEs.
problem Cold start and data sparsity issues in CF-based recommender systems.
method Proposes a variational collaborative model (VCM) that synchronously links two VAEs.
result VCM outperforms state-of-the-art methods on real-life datasets.
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.
Topic models are popular for modeling discrete data (e.g., texts, images, videos, links), and provide an efficient way to discover hidden structures/semantics in massive data. One of the core problems in this field is the posterior inference for individual data instances. This problem is particularly important in strea…
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.
We develop algorithms to learn non-linear dynamical systems without mixing assumptions.
problem Learning non-linear dynamical systems from dependent data.
method We introduce an offline algorithm and a one-pass streaming method with SGD-RER.
result Our methods achieve optimal or near-optimal performance for learning non-linear systems.
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.
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.
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.
New method learns dynamic node embeddings from temporal networks.
problem Temporal networks' temporal information is often ignored or approximated.
method Continuous-time dynamic network embeddings (CTDNEs) using temporal walks.
result CTDNEs achieve an average gain of 11.9% in AUC across all methods and graphs.
Proposes a novel classifier for probabilistic record linkage.
problem Probabilistic record linkage across databases.
method Graphical model based on mixture of Poisson distributions with latent variables, using gamma priors and supervised labels.
result Classifier works effectively with sparse and streaming data.
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.
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.
RNN-MAS predicts YouTube video popularity by integrating multiple sources of external influence.
problem Predicting popularity in asynchronous social media streams with multiple external influences.
method Recurrent Neural Network (RNN) for modeling asynchronous streams, integrating multiple sources of external influence.
result RNN-MAS outperforms state-of-the-art YouTube popularity prediction system by 17%.
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, memory, and -approximation; adaptive complexity reduction.
result Achieves -approximation with 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.
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.
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.
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.
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 memory with and memory with .
result History PCA converges faster and performs better than existing methods.
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.
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streamscs.LG
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.
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.
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.
Paper proposes a method to estimate coverage in data streams.
problem Estimating coverage in data streams with limited storage.
method Modified CVM algorithm for estimating coverage in streaming settings.
result The method efficiently estimates coverage in data streams.
Adapts DPMM for fast streaming data clustering.
problem Clustering streaming data with time-dependent statistics.
method Adapts DPMM and sampling-based inference for online clustering.
result Obtains state-of-the-art results in speed and accuracy.
Bayesian model identifies outliers and determines tensor rank in streaming data.
problem Outliers and over-fitting in streaming tensor factorization.
method Variational Bayesian Inference for robust tensor rank determination and outlier identification.
result Model accurately identifies sparse outliers and determines tensor rank.
Detects synchronized behavior in streaming data.
problem Tracking synchronized behavior in time-stamped tuples.
method AugSplicing algorithm for streaming dense block detection.
result Effective and robust in detecting anomalous behavior.
New method for estimating higher-order network dependencies in streaming data.
problem Estimating higher-order dependencies in massive, dynamic, and streaming networks.
method Adaptive sampling and unbiased estimators for streaming networks, with a James-Stein shrinkage estimator.
result Our approach outperforms baseline methods in estimating higher-order network structure from streaming data.
Estimates customer segments from continuous marketing data streams.
problem Analyzing large, continuously updated marketing data streams.
method oFMLR: online estimation of finite mixture of logistic regression models.
result oFMLR provides interpretable customer segment clustering.
New framework improves fraud prediction with incremental data balancing for massive data streams.
problem Class imbalance problem in massive imbalanced data streams.
method Incremental data balancing framework using Racing Algorithm for automated balancing and Random Forest for classification.
result Better results than Batch mode on European Credit Card dataset.