Develops large-sample theory for non-stationary source separation.
problem Lack of large-sample results for non-stationary source separation methods.
method Large-sample theory for NSS-JD method under specific assumptions.
result Consistency of unmixing estimator and its convergence to Gaussian distribution.
In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To spe…
A new method for binary ICA using non-stationary sources.
problem Independent component analysis of binary data.
method Linear mixing model in latent space, followed by binary observation model with non-stationary sources.
result Proves non-identifiability with few observed variables but identifies with more variables.
Flexible GP model improves wind power prediction accuracy.
problem Accurate probabilistic prediction of wind power for grid stability.
method Heteroscedastic non-stationary Gaussian process with generalised spectral mixture kernel.
result The proposed model outperforms conventional GP models in wind power prediction.
The economic crisis in Argentina around year 2002 provides a unique opportunity for Econophysics studies. The available data on individual income are analyzed to show that they correspond to non stationary states. However, the rather restricted size of the data survey imposes difficulties that must be overcome through …
Memory-based models can learn to approximate Bayes-optimal predictors for non-stationary data.
problem Learning from non-stationary data with unobserved switching points.
method Memory-based neural models, including Transformers, LSTMs, and RNNs, trained to minimize log loss.
result Memory-based models can accurately approximate known Bayes-optimal algorithms and perform Bayesian inference over latent switching points.
New TS algorithms improve performance in non-stationary multi-armed bandit problems.
problem Sequential decision-making with evolving action rewards.
method Sliding-window Thompson sampling approaches with different priors.
result Unified regret upper bound for arbitrary non-stationary MABs.
Paper proposes an unsupervised adaptation algorithm for non-stationary batch data.
problem Gradual concept drift in non-stationary data sources.
method Iterative unsupervised algorithm for model adaptation.
result Improved performance of adapted models compared to unadapted ones.
Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed. Our objective is to estimate the distributions of the sources. A standard approach to this problem is to model the …
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available. Both individual sources and mixing filters need to be estimated. In addition, a mixture may con…
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.
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e.g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data. The key insight of our optimization scheme for reconstructing the time-frequency information is that when a nonparame…
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.
Unified framework for Bayesian online learning in changing conditions.
problem Probabilistic online learning in non-stationary environments.
method BONE framework with three modelling choices and two algorithmic choices.
result Framework allows for reinterpreting and proposing new methods.
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.
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.
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.
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.
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.
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.
Study combines VICReg and TNC for better encoding of non-stationary seismic signals.
problem Ineffective self-supervised learning on non-stationary time series.
method Combines VICReg and Temporal Neighborhood Coding (TNC).
result Effective for self-supervised learning on non-stationary seismic signals.
We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…
Unified approach for learning state representations from streaming data.
problem Learning reusable state representations from high-dimensional, non-stationary data.
method Unified mathematical formulation for learning latent relations, enabling flexible and principled shaping of latent space.
result Improved understanding and evaluation of existing unsupervised learning approaches.
New method bypasses global fit for LISA's Galactic binaries, extracting population parameters directly.
problem Disentangling LISA's Galactic binary sources from backgrounds in a computationally intensive process.
method Simulation-based approach using normalizing flow to infer population parameters.
result Direct inference of population parameters from LISA's frequency strain series.
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.
This paper develops the first method for the exact simulation of reflected Brownian motion (RBM) with non-stationary drift and infinitesimal variance. The running time of generating exact samples of non-stationary RBM at any time t is uniformly bounded by O(1/γˉ2) where γˉ is the average drift of…
Unified review of methods for inferring non-stationary process parameters.
problem Inferring parameters of non-stationary processes without a known model.
method Unified review and categorization of algorithms for Parameter Inference from a Non-stationary Unknown Process (PINUP).
result Simple statistical features can perform well on non-stationary systems, highlighting gaps in existing methods.
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.
Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time. We consider the problem of identifying the K most attractive items and…
In this paper, we obtain the finite-horizon and infinite-horizon ruin probability asymptotics for risk processes with claims of subexponential tails for non-stationary arrival processes that satisfy a large deviation principle. As a result, the arrival process can be dependent, non-stationary and non-renewal. We give t…
Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.
problem Capturing non-stationary dependencies in point process data.
method Approximates the influence kernel with a novel low-rank decomposition and introduces a log-barrier penalty to maintain non-negativity.
result Demonstrates superior performance and computational efficiency compared to state-of-the-art methods.
Paper tackles non-stationary kernelized bandits with near-optimal algorithm.
problem Minimizing regret in a time-varying reward function.
method Near-optimal algorithm with a novel restarting phased elimination with random permutation (R-PERP).
result Regret upper bound matches the lower bound, making the algorithm near-optimal.
The paper provides exact multivariate amplitude distributions for non-stationary Gaussian or algebraic fluctuations.
problem Capturing the statistical properties of fluctuating correlations in non-stationary systems.
method Developed a random matrix model to average multivariate amplitude distributions from short time scales to large time scales.
result Explicit multivariate distributions for non-stationary correlation systems are provided, capturing the degree of non-stationarity.
Efficient GP framework for scalable non-stationary processes.
problem Heavy memory and computational requirements in Gaussian process regression for large data sets.
method Exploits structure in the kernel matrix, uses multiple sets of non-equidistant inducing points, and employs Toeplitz and Kronecker structure for efficient inference.
result Demonstrated scalability on numerical examples and large biomedical datasets.
A new fuzzy time series method for non-stationary data.
problem Forecasting in non-stationary environments with concept drift.
method Non-Stationary Fuzzy Time Series (NSFTS) with time-varying parameters.
result The method can adapt to dynamic changes in the stochastic process.
Extends geometric approach to model non-stationary extremal dependence.
problem Capturing evolving extremal dependence in multivariate data.
method Geometric framework for non-stationary multivariate extreme value modelling.
result Framework can capture various dependence forms and is robust to different model formulations.
Predictive sampling improves on Thompson sampling for non-stationary bandit environments.
problem Thompson sampling fails in non-stationary bandit environments.
method Proposes predictive sampling, which deprioritizes actions based on information loss rate.
result Predictive sampling outperforms Thompson sampling in all tested non-stationary environments.
New RL algorithm tackles non-stationary environments with flexible policy updates.
problem Non-stationary reinforcement learning with time-varying rewards and transition probabilities.
method Model-free policy-based algorithm NS-NAC with restart-based exploration and dynamic learning rates.
result Dynamic regret of ildeO(∣S∣1/2∣A∣1/2ΔT1/6T5/6) for both algorithms. 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.
MetaCURL tackles non-stationary MDPs with optimal dynamic regret.
problem Online learning in non-stationary Markov decision processes.
method MetaCURL uses a meta-algorithm with multiple black-box algorithms and a sleeping expert framework.
result Achieves optimal dynamic regret without prior knowledge of MDP changes.
We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed data is modeled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting …
The study analyzes online predictions for non-stationary time series under model misspecification.
problem Analyzing predictive properties of statistical methods in non-stationary time series under model misspecification.
method Defining Kullback-Leibler risk, proving minimax predictive densities for dynamic models, extending results to multiple predictive densities.
result Dynamic random walk models produce exact minimax predictive densities under Gaussian assumptions and semi-martingale processes.
Study non-stationary online auctions with semi-bandit feedback.
problem Maximize revenue in a non-stationary online second price auction.
method Develops an algorithm to handle non-stationary private value distributions.
result Achieves nearly optimal non-stationary regret bound.
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.
New algorithm tackles non-stationary delayed feedback in recommender systems.
problem Challenges in learning from delayed feedback in non-stationary environments.
method Developed a UCRL-based algorithm for non-stationary, delayed bandits with intermediate observations.
result Sublinear regret guarantees for the proposed algorithm in non-stationary delayed environments.
Proposes a flexible MGP model for dynamic, sparse correlations.
problem Handling dynamic and sparse correlations in multivariate data.
method Non-stationary MGP with dynamic spike-and-slab prior and EM algorithm.
result Captures dynamic and sparse correlations effectively.
This paper proposes non-stationary factor models for financial stress in the UK.
problem Managing financial vulnerabilities in the UK's complex financial system.
method Creation of non-stationary factor models to capture financial stress.
result Non-stationary factor models can better capture financial stress, especially tail events.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…