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.
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.
Quadratic memory needed for linear prediction in streaming model.
problem Scalable memory-efficient linear prediction in streaming model.
method Memory lower bound for finding orthogonal vectors and estimates on Grassmannian packing.
result Problems cannot be solved by scalable memory-efficient streaming algorithms.
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.
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) memory, and (1/2)-approximation; adaptive complexity reduction. result Achieves (1/2)-approximation with O(k) memory and low adaptive complexity. This research generates synthetic data streams for handling concept drifts and novel classes.
problem Handling concept drifts and novel classes in dynamic data streams.
method Synthetic data stream generation for both concept drifts and novel classes.
result Demonstrates the effectiveness of unsupervised drift detectors in open set recognition.
AdaOja improves Oja's algorithm for streaming PCA with adaptive learning rates.
problem Lack of standardized learning rates in Oja's algorithm for streaming PCA.
method Proposes AdaOja, a novel learning rate scheme for Oja's method.
result AdaOja outperforms common learning rate choices and performs comparably to state-of-the-art algorithms.
We adapted the Covertype data set for unsupervised learning.
problem Lack of suitable unsupervised learning data sets.
method Transformed the Covertype data set into the Wilderness Area data set.
result The Wilderness Area data set is more suitable for unsupervised learning.
New algorithm optimizes linear system estimation from single trajectory.
problem Estimating LTI systems from a single trajectory.
method SGD with Reverse Experience Replay (SGD−RER) result Optimal guarantees for parameter and prediction errors.
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algo…
MSTREAM detects anomalies in multi-aspect data streams.
problem Detecting unusual group anomalies in dynamic multi-aspect data.
method Online streaming multi-aspect anomaly detection framework.
result MSTREAM outperforms state-of-the-art baselines.
Study private submodular maximization in streaming data.
problem Private maximization of submodular functions in streaming data.
method Established differentially private baselines and derived better trade-offs for decomposable submodular functions.
result Improved trade-offs between privacy and utility for decomposable submodular functions.
Enhash detects concept drift in data streams quickly and efficiently.
problem Detecting abrupt, gradual, virtual, or recurring events in data streams.
method Uses projection hash to insert incoming samples and detects concept drift.
result Enhash has competitive performance and moderate resource requirements compared to existing ensemble learners.
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.
New method detects when models influence their own drift in real-time data streams.
problem Models can induce concept drift in real-time data streams.
method CheckerBoard Performative Drift Detection (CB-PDD)
result CB-PDD effectively detects performative drift in real-time data streams.
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.
New algorithm improves feature selection for streaming data.
problem Traditional OSFS methods assume all data available at runtime, but features and samples stream concurrently.
method Introduces Geometric Online Adaption (GOA) for concurrent streaming of features and samples.
result GOA outperforms SAOLA on various datasets and in the OSFS-SS setting.
An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed. However, due to various factors when collecting and pre-processing data from different views, the streaming view setting, in which views arrive in a streaming manner, is becoming more common. By ass…
New algorithm clusters streaming data efficiently.
problem Challenges in clustering streaming data.
method Online clustering algorithm for unknown number of clusters.
result Produces partitions close to full data clustering.
Bayesian coresets help reduce network intrusion detection data size.
problem Large network intrusion detection data sets hinder Bayesian learning.
method Use Bayesian coresets to reduce data size while maintaining accuracy.
result Bayesian coresets improve learning accuracy and reduce storage needs.
Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which assume that we condition on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze strea…
Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long. Furthermore, as parameter tuning is problematic in a streaming setting, suitab…
A new method for predicting uncertainties in stream networks.
problem Uncertainty quantification in spatiotemporal graphs with directional flow constraints.
method Spatio-Temporal Adaptive Conformal Inference (STACI) integrating network topology and temporal dynamics.
result STACI effectively balances prediction efficiency and coverage, outperforming existing methods.
We bring the theory of rough paths to the study of non-parametric statistics on streamed data. We discuss the problem of regression where the input variable is a stream of information, and the dependent response is also (potentially) a stream. A certain graded feature set of a stream, known in the rough path literature…
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.
In supervised machine learning, an agent is typically trained once and then deployed. While this works well for static settings, robots often operate in changing environments and must quickly learn new things from data streams. In this paradigm, known as streaming learning, a learner is trained online, in a single pass…
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.
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
New method creates coresets for deep neural networks efficiently.
problem Efficiently handling large data streams with limited resources.
method Cardinality-constrained bilevel optimization for deep neural networks.
result Demonstrated efficient generation of coresets for deep neural networks.
Paper improves Oja's algorithm for Markovian data streams.
problem Estimating the top eigenvector of a covariance matrix from Markovian data.
method Improves Oja's algorithm for streaming PCA with Markovian dependence.
result First sharp rate for Oja's algorithm on entire data stream, removing sample size dependence.
INCAD clusters and detects anomalies in streaming data without thresholds.
problem Clustering and anomaly detection for streaming data with unknown clusters and thresholds.
method Probabilistic clustering and anomaly detection in a streaming model.
result More reliable definition of normal vs abnormal behavior in streaming data.
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.
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…
Robust PCA method works under uncertain covariance.
problem Principal component analysis under uncertain covariance.
method Robust streaming PCA with temporal uncertainty set.
result Noisy power method is rate-optimal in our setting.
Paper proposes a distributed method to estimate principal eigenvector from high-rate streaming data.
problem Estimating principal eigenvector from high streaming data rate.
method Distributed Krasulina (D-Krasulina) and mini-batch extension (DM-Krasulina) methods.
result Achieves optimal estimation error rates under high streaming conditions.
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages t…
A method for learning rankings in non-stationary data streams.
problem Learning preferences in a population that changes over time.
method Generalized Borda algorithm for non-stationary ranking streams.
result Bounds on the minimum number of samples required to output the ground truth.
In this paper we propose a new algorithm for streaming principal component analysis. With limited memory, small devices cannot store all the samples in the high-dimensional regime. Streaming principal component analysis aims to find the k-dimensional subspace which can explain the most variation of the d-dimensiona…
Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full n×n kernel matrix is constructed over n data points, but this…
CDRE estimates density ratios in streaming data without historical samples.
problem Online learning with shifting data distributions.
method Iterative estimation of density ratios between initial and current distributions.
result CDRE outperforms standard DRE in estimating divergences between distributions.
New method combines deep learning and streaming learning for better incremental learning.
problem Catastrophic forgetting in deep neural networks when updated incrementally.
method Combining streaming linear discriminant analysis with deep learning.
result Outperforms incremental batch learning and streaming learning on ImageNet and CORe50.
The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related…
We present a streaming model for large-scale classification (in the context of ℓ2-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The ℓ2-SVM is known to have an equivalent formulation in …
New fair PCA method using streaming algorithms with statistical guarantees.
problem Perform PCA while ensuring fair projected distributions.
method Formulated new notion PAFO learnability, proposed fair noisy power method (FNPM) for memory efficiency.
result First statistical guarantee for fair PCA in streaming setting.
Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive. In this paper, we argue that a stable manifold can be learned using only a fraction of the stream…