arXiv research

A new method for estimating SW from streaming data.

New algorithm samples matrix rows proportional to their ℓ_p norm in a turnstile data stream.

Paper improves Oja's algorithm for Markovian data streams.

Paper proposes a method to estimate coverage in data streams.

New online method estimates OT distances from sample streams.

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…

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete unde…

CROC identifies the earliest-changing stream as the root cause in multi-stream data.

Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an onli…

Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However, in many practical applications such as online social networks, networks are massive, dynamic, and naturally streaming, where pairwise inter…

Optimal sampling reduces power grid data analysis costs.

Adapts DPMM for fast streaming data clustering.

New robustness certificates for streaming models with a sliding window.

CDRE estimates density ratios in streaming data without historical samples.

StreaMRAK improves KRR for streaming data.

Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a challenge for such methods, owing to the high computational complexity involved. Moreover, most methods operate under the assumption that the…

New algorithm learns and unlearns from streaming data efficiently.

Paper proposes an attention sampler for reducing attention mechanism computation.

This paper considers the problem of estimating the principal eigenvector of a covariance matrix from independent and identically distributed data samples in streaming settings. The streaming rate of data in many contemporary applications can be high enough that a single processor cannot finish an iteration of existing …

This work provides improved guarantees for streaming principle component analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled independently from distributions satisfying $\mathbb{E}[A_i] = Σ$ for $Σ\succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time single-pass streaming algorithm …

When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional deep neural networks, which suffer from catastrophic forgetting when they are incrementally updated, with new knowledge overwriting established…

Enhash detects concept drift in data streams quickly and efficiently.

LOBDIF predicts limit order book events using a diffusion model.

PEAK tests means of multiple data streams with sequential betting.

Estimates bisimulation metrics from sample streams, not full transition models.

New algorithms for streaming bandits with limited memory.

New algorithms for IV regression with streaming data, avoiding matrix inversions.

Online Streaming Feature Selection (OSFS) is a sequential learning problem where individual features across all samples are made available to algorithms in a streaming fashion. In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introd…

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

Unified study of stateful replay for streaming learning, reducing forgetting by 2-3x.

Survey of robust streaming techniques and their relationships.

We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …

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…

We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/ε^2)$ sample complexity. Furthermore, we give a memory efficient streaming algorithm based on classical Oja…

A new algorithm for K-means clustering in evolving data streams.

New method for efficient online variational estimation in streaming data.

Federated learning for data streams tackles real-time learning from IoT devices.

InQMAD detects anomalies in streaming data using quantum measurements and density matrices.

Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. In streaming contexts, learning must also …

A new method for real-time CCA on streaming data.

Online TERM improves robustness and fairness in streaming data.

Paper tackles active labeling for partial supervision.

We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize the sample complexity of the protocol subject to a memory constraint, and (2) A {\em distributed model} where the data samples reside at mul…

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…

Signatures simplify analysis of evolving data streams.

We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data. Our approach, Adaptive Nonparametric Variational Autoencoder (AdapVAE), learns the cluster membership through a Bayesian Nonparametric (BN…

New algorithm optimizes linear system estimation from single trajectory.

Novel algorithm SAODE improves high-dimensional stream classification in seasonal data.