On-device research index

arXiv research

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.

168,695 papers · 148 categories

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48 results for “Non-Stationary Detection”

Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Banditsstat.ML

Proposes a new TS algorithm for non-stationary bandits using KS tests.

problem Non-stationary multi-armed bandit problems.

method Active detection of change points using KS tests and adaptive Thompson Sampling.

result Sub-linear regret demonstrated for the two-armed bandit case.

Thompson Sampling Kolmogorov-Smirnov Test Non-Stationary Bandits Change Detection Wireless Edge-Computing

2021-05-30abs ↗pdf ↗

OML-AD: Online Machine Learning for Anomaly Detection in Time Series Datacs.LG

OML-AD detects anomalies in non-stationary time series data.

problem Anomaly detection in non-stationary time series data.

method Online machine learning for anomaly detection.

result OML-AD outperforms state-of-the-art methods in accuracy and efficiency.

anomaly detection time series non-stationary online machine learning River library

2024-09-15abs ↗pdf ↗

Multiscale Non-stationary Stochastic Banditsstat.ML

Proposes a new algorithm for non-stationary bandits.

problem Non-stationary reward distributions in contextual bandits.

method Multiscale changepoint detection for adaptive learning.

result Regret bound analysis and superior performance in experiments.

bandits non-stationary multiscale changepoint adaptive

2020-02-13abs ↗pdf ↗

An Efficient Variant of One-Class SVM with Lifelong Online Learning Guaranteesstat.ML

SONAR improves outlier detection for streaming data with strong theoretical guarantees.

problem Outlier detection for non-stationary streaming data with high Type I/II errors.

method SONAR is an efficient SGD-based OCSVM solver with strong convex regularization and lifelong learning guarantees.

result SONAR outperforms traditional OCSVM in Type I/II error rates under non-stationary data.

outlier detection streaming data SGD OCSVM lifelong learning

2025-12-11abs ↗pdf ↗

Non-Stationary Dueling Banditscs.LG

Algorithm minimizes regret in non-stationary dueling bandits with unknown parameters.

problem Minimizing regret in dueling bandits with time-varying preferences.

method Proposes Beat the Winner Reset algorithm and meta-algorithms DETECT and Monitored Dueling Bandits.

result Proves bounds on expected weak and strong regret for non-stationary dueling bandits.

dueling bandits non-stationary regret minimization meta-algorithms Condorcet winner

2022-02-02abs ↗pdf ↗

DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Banditscs.LG

DAL enhances black-box bandit algorithms for non-stationary environments.

problem Non-stationary environments in bandit problems.

method DAL combines any stationary bandit algorithm with a change detector.

result DAL consistently outperforms state-of-the-art methods in various non-stationary scenarios.

bandit problems non-stationary environments change detection order-optimal regret

2025-01-31abs ↗pdf ↗

Unifying Clustered and Non-stationary Banditscs.LG

Unified approach for non-stationary and clustered bandits.

problem Solving non-stationary and clustered bandits with overlapping solutions.

method Test of homogeneity for seamless integration of non-stationary and clustered bandits.

result Unified solution framework for change detection and cluster identification.

bandits non-stationary clustering test of homogeneity regret analysis

2020-09-05abs ↗pdf ↗

Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?cs.LG

Master algorithm fails to detect non-stationarity in practical settings.

problem Non-Stationary Reinforcement Learning without prior knowledge.

method Master algorithm tested under various conditions, including piecewise stationary multi-armed bandits.

result Master's non-stationarity detection is ineffective for practical horizons, leading to performance similar to random restarting.

Non-Stationary Reinforcement Learning Master Algorithm Non-stationarity Detection Piecewise Stationary Quickest Change Detection

2024-10-17abs ↗pdf ↗

Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcs.LG

Paper tackles concept drift in Federated Learning, improving model performance.

problem Concept drift in real-world data makes existing Federated Learning methods ineffective.

method Introduces a multiscale algorithm combining extit{FedAvg} and extit{FedOMD} with non-stationary detection and adaptation.

result Achieves dynamic regret of $\Tilde{\mathcal{O}} ( \min \{ \sqrt{LT} , Δ^{\frac{1}{3}}T^{\frac{2}{3}} + \sqrt{T} \})$ for

T

rounds.

Federated Learning Concept Drift Non-Stationary Detection Adaptation Dynamic Regret

2022-11-22abs ↗pdf ↗

DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guaranteescs.LG

DARLING tackles non-stationary RL with guarantees, improving dynamic regret.

problem Non-stationary reinforcement learning in unknown change points.

method Detection Augmented Reinforcement Learning (DARLING) for tabular and linear MDPs.

result DARLING matches minimax lower bounds in tabular and linear MDPs.

reinforcement learning non-stationary dynamic regret tabular MDPs linear MDPs

2026-04-17abs ↗pdf ↗

An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledgestat.ML

Algorithm adapts to non-stationary rewards without prior knowledge.

problem Optimizing decisions in non-stationary environments without prior knowledge of changes.

method Optimization-based algorithm that restarts when non-stationarity is detected.

result Achieves tighter dynamic regret bound and is nearly minimax optimal.

non-stationary bandits optimization dynamic regret kernel methods neural networks

2022-05-29abs ↗pdf ↗

Reinforcement Learning in Non-Stationary Environmentscs.LG

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environme…

2019-05-10abs ↗pdf ↗

Optimal training-conditional regret for online conformal predictionmath.ST

Study online conformal prediction for non-stationary data with optimal training-conditional regret.

problem Online prediction for non-stationary data streams with unknown distribution drift.

method Proposes split-conformal and full-conformal algorithms that adapt to drift detection and incorporate stability for online learning.

result Proves minimax-optimal regret for online full conformal algorithm under appropriate restrictions.

online prediction non-stationary data conformal prediction regret minimization drift detection

2026-02-18abs ↗pdf ↗

Identifiable Markov Switching Models with Instantaneous Effects and Exponential Familiesstat.ML

New model identifies regimes in non-stationary data.

problem Identifying latent regimes in non-stationary systems with instantaneous effects.

method Identifiable Markov Switching Models with exponential family noise.

result Established identifiability of latent regimes and causal structures.

Markov Switching Models Exponential Family Latent Regimes Instantaneous Effects Non-stationary Data

2026-06-01abs ↗pdf ↗

Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processescs.LG

Improved algorithm detects changes in RL environments with non-stationary MDPs.

problem Learning in non-stationary reinforcement learning environments.

method R-BOCPD-UCRL2 algorithm for MDPs with multinomial state transitions.

result Near-optimal theoretical guarantees in terms of false-alarm rate and detection delay.

Bayesian Change-point detection Reinforcement learning Non-stationary Markov decision process

2023-04-01abs ↗pdf ↗

Finite-Horizon Quickest Change Detection Balancing Latency with False Alarm Probabilitycs.IT

Optimizes latency and false alarm probability in change detection problems.

problem Balancing latency and false alarms in non-stationary environments.

method Develops order-optimal change detectors under specified latency and false alarm levels.

result Derives a universal lower bound on latency and develops order-optimal detectors.

quickest change detection non-stationary environments false alarm probability latency order-optimality

2025-11-16abs ↗pdf ↗

On Slowly-varying Non-stationary Banditscs.LG

New algorithm for non-stationary bandits with slow drifts.

problem Minimizing dynamic regret in non-stationary bandits with slowly varying rewards.

method Extends Successive Elimination to non-stationary bandits with a novel gap profile characterization.

result First instance-dependent regret upper bound for slowly varying non-stationary bandits.

non-stationary bandits slowly varying dynamic regret successive elimination minimax optimality

2021-10-25abs ↗pdf ↗

Non-Stationary Lipschitz Banditsstat.ML

New algorithm tracks changes in infinite action space rewards.

problem Non-stationary Lipschitz bandits with infinite actions.

method Adaptive tracking of significant shifts using hierarchical discretization.

result Achieves minimax-optimal dynamic regret bound of

\mathcal{\widetilde{O}}( ilde{L}^{1/3}T^{2/3})

Lipschitz bandits non-stationary dynamic regret significant shifts hierarchical discretization

2025-05-24abs ↗pdf ↗

Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Seriesstat.ME

Kernel-based tests detect dependencies in multivariate time series, including stationary and non-stationary data.

problem Detecting dependencies in multivariate time series data, especially non-stationary data.

method Kernel-based statistical tests of joint independence, extending dHSIC to handle both stationary and non-stationary processes.

result Robustly uncovers significant higher-order dependencies in synthetic and real-world data.

time series joint independence kernel methods Hilbert-Schmidt independence criterion non-stationary data

2023-05-15abs ↗pdf ↗

A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Banditscs.LG

A new framework for risk-aware multi-armed bandits tackles volatile environments.

problem Volatility in healthcare and finance makes naive reward maximization unreliable.

method Risk-aware strategies with adaptive risk measures and change-point detection.

result Finite-time theoretical guarantees and asymptotic regret bound of order

ilde O(\sqrt{K_T T})

multi-armed bandits non-stationary risk-aware change-point detection regret bound

2023-10-24abs ↗pdf ↗

Multifractal temporally weighted detrended partial cross-correlation analysis to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factorsphysics.soc-ph

New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.

problem Bias in cross-correlation analysis due to common external factors.

method Multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA).

result MF-TWDPCCA accurately detects intrinsic cross-correlations between non-stationary time series.

cross-correlation non-stationary time series common external factors multifractal analysis power-law

2020-05-31abs ↗pdf ↗

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selectionstat.ML

Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (VARs) for modelling the process between changepoints (CPs) and give an upper bound on the approximation error of such models. The resulting …

2018-05-14abs ↗pdf ↗

Algorithm Design for Online Meta-Learning with Task Boundary Detectioncs.LG

New algorithm for online meta-learning with task boundary detection.

problem Adapting to new tasks in a non-stationary environment.

method Two detection mechanisms for task switches and distribution shift; online model updates based on current data.

result Achieves sublinear task-averaged regret under mild conditions.

online meta-learning task boundary detection non-stationary environments regret minimization

2023-02-02abs ↗pdf ↗

Detection Augmented Bandit Procedures for Piecewise Stationary MABs: A Modular Approachcs.AI

New algorithms detect changes in non-stationary MABs for better performance.

problem Non-stationary MAB environments where arm reward distributions change over time.

method Modular Detection Augmented Bandit (DAB) procedures with improved performance lower bounds.

result Modular DAB procedures achieve order-optimal regret bounds for various change detectors and bandit algorithms.

Bandit problems Change detection Piecewise stationary MABs Asymptotic analysis Sub-Gaussian rewards

2025-01-02abs ↗pdf ↗

Differentiable Segmentation of Sequencescs.LG

Differentiable segmented models for non-stationary data.

problem Estimating change points in non-stationary sequential data.

method Formulated a relaxed variant of segmented models that enables gradient descent for all parameters, including segmentation.

result Successfully learned all tasks with standard gradient descent algorithms.

Differentiable Segmentation Non-Stationary Data Gradient Descent Change Point Detection Concept Drift

2020-06-23abs ↗pdf ↗

Synthetic Non-stationary Data Streams for Recognition of the Unknowncs.LG

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.

data streams concept drifts novel classes open set recognition synthetic data

2025-05-19abs ↗pdf ↗

A Switching Beamformer for Highly Non-Stationary Environmentseess.SP

Adaptive beamforming collapses in highly non-stationary environments, but the Universal Switching Beamformer resolves this by dynamically adjusting memory length.

problem Adaptive beamforming performance degrades in highly non-stationary environments.

method Integrating sequential prediction into the beamforming architecture.

result The USB achieves agility and precision in tracking highly non-stationary scenes.

beamformer adaptive non-stationary tracking memory length

2026-06-07abs ↗pdf ↗

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosisstat.ML

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Rec…

2017-08-08abs ↗pdf ↗

The Hiremath Early Detection (HED) Score: A Measure-Theoretic Evaluation Standard for Temporal Intelligencestat.ML

HED Score improves temporal evaluation of detection accuracy.

problem Temporal agnosticism in existing evaluation frameworks for non-stationary processes.

method Measure-theoretic HED Score integrating exponentially decaying kernel over posterior probability stream.

result HED Score achieves 388.8% improvement over ROC/AUC on NSL-KDD benchmark.

HED Score temporal evaluation non-stationary processes temporal agnosticism exponential decay

2026-04-05abs ↗pdf ↗

Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systemscs.LG

RAID algorithm detects anomalies in real-time IoT systems.

problem Anomaly detection limitations in multivariate dynamic processes.

method Adapts to non-stationary effects and handles data drift.

result Improved detection accuracy and root cause isolation.

anomaly detection real-time IoT data drift dynamic processes root cause isolation

2023-04-06abs ↗pdf ↗

Anomaly Detection and Removal Using Non-Stationary Gaussian Processesstat.ML

This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…

2015-07-02abs ↗pdf ↗

Associative Memory for Non-Stationary Environments: A Self-Sizing Generalization of Hopfield Networksstat.ML

A new memory system handles non-stationary environments by self-sizing and retaining memories.

problem Non-stationary environments where memories arrive over time and must be distinguished from noise.

method A self-sizing continual associative memory that generalizes Hopfield's network, handling adaptation and novelty.

result The memory system grows to the intrinsic memory demand of the environment and retains memories without forgetting.

associative memory non-stationary environments Hopfield networks self-sizing novelty detection

2025-07-08abs ↗pdf ↗

Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systemscs.LG

Algorithm minimizes control regret for non-stationary LQR systems.

problem Control of non-stationary LQR systems with unknown dynamics.

method Adaptive non-stationarity detection and OLS estimator with small bias.

result Achieves optimal dynamic regret of $ ilde{\mathcal{O}}\left(V_T^{2/5}T^{3/5} ight)$.

Dynamic Regret Non-stationary LQR Adaptive Control Contextual Bandits Least Squares Estimator

2021-11-06abs ↗pdf ↗

Change-Point Detection on Hierarchical Circadian Modelsstat.ML

This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to the dimensionality problem, when the time between change-points is on the order of the dimension of the model parameters, drifts in the und…

2018-09-11abs ↗pdf ↗

Mining Illegal Insider Trading of Stocks: A Proactive Approachq-fin.ST

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…

2018-07-02abs ↗pdf ↗

Forecasting Inflation with Microdata: An Adaptive Machine Learning Approachecon.GN

Microdata improves inflation forecasts after major shocks, study finds.

problem Forecasting inflation in a non-stationary environment with microeconomic data.

method Developed a scan test to detect periods of micro forecast outperformance, combined with adaptive machine learning.

result Micro forecasts improve inflation predictions after major shocks, especially after 2020.

Inflation Forecasting Microdata Machine Learning Scan Test Adaptive Algorithms

2026-07-14abs ↗pdf ↗

Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Datastat.ML

This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical…

2017-02-06abs ↗pdf ↗

Efficient Change-Point Detection for Tackling Piecewise-Stationary Banditsstat.ML

We introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, with an efficient, parameter-free, changepoint detector, the Bernoulli Generalized Likelihood Ratio Test, for which we provide new theoretica…

2019-02-05abs ↗pdf ↗

A Definition of Non-Stationary Banditscs.LG

New definition resolves ambiguity in non-stationary bandit classification.

problem Ambiguity in classifying non-stationary bandits using existing definitions.

method Introducing a formal definition that resolves ambiguity and provides a unified approach.

result Unified approach applicable to both Bayesian and frequentist formulations, resolves classification issues.

non-stationary bandits regret dynamic regret weak regret agent performance

2023-02-23abs ↗pdf ↗

Non-Stationary Functional Bilevel Optimizationstat.ML

SmoothFBO tackles non-stationary functional bilevel optimization.

problem Current FBO methods are limited to static offline settings and perform poorly in online, non-stationary scenarios.

method SmoothFBO introduces a time-smoothed stochastic hypergradient estimator with a window parameter to handle non-stationarity.

result SmoothFBO achieves sublinear regret and outperforms existing methods in non-stationary hyperparameter optimization and model-based reinforcement learning.

functional bilevel optimization non-stationary optimization hyperparameter optimization reinforcement learning sublinear regret

2026-01-21abs ↗pdf ↗

Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewardscs.LG

A new algorithm tackles delayed combinatorial semi-bandit with causal relations.

problem Optimizing decisions in a non-stationary environment with delayed and causally related rewards.

method Formalized as a non-stationary delayed combinatorial semi-bandit problem, the approach models causal relations with a directed graph in a stationary structural equation model. The agent learns these relations from delayed feedback to optimize decisions.

result Proved a regret bound for the proposed algorithm's performance.

delayed combinatorial semi-bandit causal relations structural equation model regret bound feedback delay

2023-07-18abs ↗pdf ↗

On change point detection using the fused lasso methodmath.ST

In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances. The focus is on segmentation of a non-stationary time series with respect to changes in these model parameters. This change point detection and estimation pr…

2014-01-21abs ↗pdf ↗

Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detectionstat.ML

Detects anomalies in vector fields without distributional assumptions.

problem Detecting anomalies in high-dimensional, non-stationary vector fields.

method Optimal Karhunen-Loeve expansion, multilevel orthogonal subspaces, hypothesis tests.

result Reliable anomaly detection without distributional assumptions.

vector fields anomaly detection Karhunen-Loeve multilevel non-stationary

2022-07-11abs ↗pdf ↗

Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learningstat.ML

Transformers achieve near-optimal dynamic regret in non-stationary reinforcement learning.

problem Understanding and handling non-stationary environments in reinforcement learning.

method Demonstrated that transformers can achieve nearly optimal dynamic regret bounds in non-stationary settings.

result Transformers can approximate and learn strategies for non-stationary environments, matching or outperforming existing expert algorithms.

Transformers Reinforcement Learning Non-Stationary Dynamic Regret In-Context Learning

2025-08-22abs ↗pdf ↗

Incentivized Exploration of Non-Stationary Stochastic Banditscs.LG

Study incentivizes exploration in non-stationary MAB with compensation.

problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.

method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.

result Achieves sublinear regret and compensation over time.

bandits incentivized exploration non-stationary compensation regret

2024-03-16abs ↗pdf ↗

Non-stationary Reinforcement Learning under General Function Approximationcs.LG

New algorithm tackles non-stationary reinforcement learning with general function approximation.

problem Understanding non-stationary MDPs with function approximation.

method Dynamic Bellman Eluder (DBE) dimension for complexity, sliding window mechanism, confidence set design.

result Upper bound on dynamic regret for proposed SW-OPEA algorithm.

non-stationary reinforcement learning dynamic Bellman Eluder dimension confidence set sliding window dynamic regret

2023-06-01abs ↗pdf ↗

DynaConF: Dynamic Forecasting of Non-Stationary Time Seriescs.LG

Proposes a method to forecast non-stationary time series.

problem Challenges of non-stationary conditional distributions in deep learning.

method Bayesian dynamic model + deep conditional distribution model.

result Adapts to non-stationary time series better than state-of-the-art solutions.

time series forecasting non-stationary deep learning conditional distribution Bayesian model

2022-09-17abs ↗pdf ↗

Extending echo state property for quantum reservoir computingquant-ph

New definitions of ESP for quantum reservoir computing handle non-stationary systems.

problem Traditional ESP does not apply to non-stationary systems.

method Introduce two new categories of ESP: non-stationary ESP and subset/subspace ESP.

result Demonstrates correspondence between non-stationary ESP and QRC with NARMA tasks.

quantum reservoir computing echo state property non-stationary systems subspace ESP NARMA tasks

2024-03-05abs ↗pdf ↗