A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Online algorithm identifies PDEs from noisy data snapshots.
Bayesian method improves online NARMAX model identification.
Paper addresses online identification and clustering for mixed linear regression models.
Paper accelerates nonlinear mapping in online systems with lower time complexity.
The Duffing oscillator's parameters are identified online using variational message passing.
A distributed system identification method for LTI systems using reverse experience replay.
This work compares data reduction criteria for online Gaussian Processes.
Paper proposes online learning for estimating AC network admittance matrix.
Paper tackles dynamic graph topology identification in time-varying graphs.
Online algorithms for identifying river pollution sources.
We consider online detection strategies for identifying a change point in a stream of quantum particles allegedly prepared in identical states. We show that the identification of the change point can be done without error via sequential local measurements while attaining the optimal performance bound set by quantum mec…
Study non-asymptotic BPI guarantees for online RL.
Purpose: Malicious web domain identification is of significant importance to the security protection of Internet users. With online credibility and performance data, this paper aims to investigate the use of machine learning tech-niques for malicious web domain identification by considering the class imbalance issue (i…
Efficiently identifies best policies in tabular MDPs with reduced computational cost.
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and …
An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample …
Improved SGD bounds for machine learning models with Markovian noise.
AdaptOn achieves logarithmic regret in adaptive control of unknown partially observable linear systems.
New method estimates bidirectional causal effects in large-scale systems.
Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of …
Combines offline and online learning for identifying the best arm in bandits.
Online-iForest detects anomalies in streaming data efficiently.
This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requires only basic linear algebraic manipulations at each iteration, and each subspace update can be performed in linear time in the dimension of…
PROBE optimizes best-arm identification with cheap proxies, improving sample complexity.
Method detects lithium-ion battery knee onset for early warning.
Greedy policy maximizes information in unknown linear systems.
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…
New algorithm identifies best arm in rare event scenarios.
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space. The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access t…
Two new algorithms improve online clustering of bandits by accelerating cluster identification without strong assumptions.
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret"). We merge these two objectives by providing the theoretical analysis of cost minimizing algorithms that are also delta-PAC (with a proven guaranteed bound on th…
We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regulariz…
Owing to the rapid growth of touchscreen mobile terminals and pen-based interfaces, handwriting-based writer identification systems are attracting increasing attention for personal authentication, digital forensics, and other applications. However, most studies on writer identification have not been satisfying because …
New algorithm identifies best arm in rested bandit setting.
Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changi…
Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identificat…
New method for identifying best arm in batched multi-armed bandit problems.
Bayesian method for multivariate autoregressive models with exogenous inputs.
It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …
Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting i…
We present a new algorithm based on an gradient ascent for a general Active Exploration bandit problem in the fixed confidence setting. This problem encompasses several well studied problems such that the Best Arm Identification or Thresholding Bandits. It consists of a new sampling rule based on an online lazy mirror …
Sequential tests for nonparametric hypotheses using supermartingales.
Efficiently controls unknown linear systems with black-box interactions.
Unified approach for non-stationary and clustered bandits.
This chapter covers methods for identifying and inferring graph topologies.
Improved online algorithm for convex losses with near-optimal swap regret.
Algorithm identifies best arm in combinatorial bandits with semi-bandit feedback.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…