arXiv research

Efficient streaming algorithms for robust statistics with near-optimal memory.

New robustness certificates for streaming models with a sliding window.

Survey of robust streaming techniques and their relationships.

We develop efficient algorithms for robust PCA that handle outliers.

Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. Existing streaming tensor factorization algorithms rely on least-squares data fitting and they do not possess a mechanism for tensor rank dete…

We consider the problems of robust PAC learning from distributed and streaming data, which may contain malicious errors and outliers, and analyze their fundamental complexity questions. In particular, we establish lower bounds on the communication complexity for distributed robust learning performed on multiple machine…

Paper proposes a framework to detect adversarial concept drifts under poisoning attacks.

Online TERM improves robustness and fairness in streaming data.

We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished. We develop a robust submodular algorithm STAR-…

Improved SGD for robust linear and ReLU regression with adversarial corruptions.

MaxSketch improves distinct counting in high-dimensional, noisy data streams.

We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations. While existing models assume a fixed covariance, we adopt a robust perspective where the covariance matrix belongs to a temporal uncertainty set. Under this setting, we provide fundamental limits on…

Dynamic Model Tree improves online learning for evolving data streams.

Detects synchronized behavior in streaming data.

This paper introduces online algorithms to estimate robust geometric median in large data streams.

This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fu…

Paper tackles active labeling for partial supervision.

Robust CLIP improves vision models' resistance to attacks.

Robust PCA detects anomalies and fills gaps in seasonal time series data.

This paper contributes to the challenge of learning a function on streamed multimodal data through evaluation. The core of the result of our paper is the combination of two quite different approaches to this problem. One comes from the mathematically principled technology of signatures and log-signatures as representat…

The paper develops algorithms to find a robust summary of data under deletion, achieving good approximation guarantees.

This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.

Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and so are their data. To deal with this dynamic nature of data, there exis…

New algorithm robustly optimizes data streams with heavy-tailed or infinite variance samples.

A new framework detects concept drift in streaming data.

A new control chart detects shifts in binary data streams quickly and reliably.

New unsupervised methods for anomaly detection and clustering in structured and streaming data.

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…

We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an $n$-$\ell_1$ cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and low-rank components, from com…

Robust quickest change detection method for unknown score functions.

The paper tackles robust submodular maximization under matroid constraints, providing approximation algorithms for summary extraction.

Paper introduces a fast, robust, scalable method for detecting changes in data streams.

New method improves traffic data recovery for streaming data.

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 …

The study examines how bias affects hypothesis formation in neural networks.

New mechanisms from primate vision improve neural network robustness.

Develops new techniques for learning from sequential data groups.

Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…

Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.

Triadic-OCD detects changes in data streams robustly and optimally, even in asynchronous settings.

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly…

Study examines AutoML adaptation to evolving data.

CDLEEDS detects local changes in evolving data streams for accurate feature attributions.

Paper proposes SRA algorithm for online learning robustness and adaptivity.

Synthesizes robust estimators for domain adaptation.

Paper proposes an efficient online Newton method with Nesterov's acceleration for streaming data.

A federated model learns shared archetypes from heterogeneous clients in continual learning.

One of the greatest challenges in the design of a real-time perception system for autonomous driving vehicles and drones is the conflicting requirement of safety (high prediction accuracy) and efficiency. Traditional approaches use a single frame rate for the entire system. Motivated by the observation that the lack of…