New model detects gradual changes in processes more accurately.
problem Traditional change-point models fail to identify gradual changes effectively.
method Introduces a Bayesian change-dynamic model using hierarchical models for gradual change detection.
result The model identifies gradual changes faster and more accurately than traditional models.
Paper detects hierarchical changes in latent variable models from data streams.
problem Detecting changes at three levels: data distribution, latent variables, and number of latent variables.
method Information-theoretic framework using MDL and DNML for change detection.
result Effective in detecting changes with good interpretability.
Unified framework detects changes in complex system models.
problem Accurate identification of dynamic changes in simulation models.
method Combines machine learning and process-driven simulation modeling.
result Significantly improves change point detection accuracy.
Proposes a model to detect changes in multivariate time series data.
problem Detect abrupt changes in multivariate time series data considering dependencies and correlations.
method Integrates graph neural networks into an encoder-decoder framework to model correlation structures and dynamics.
result Advantageous performance on CPD tasks over strong baselines, classifying changes as correlation or independent.
Develops a new family of signature-changing models on metric manifolds.
problem Signature changes in metric manifolds.
method One-parameter family of Lorentz-Riemann models, local expressions around change.
result Generalizes existing signature-changing models.
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalizat…
Detects data drift and outliers affecting ML model performance over time.
problem Detecting distribution changes between training and deployment datasets for machine learning models.
method Nonparametrically tests model prediction confidence distributions for changes using Change Point Models (CPMs). Also uses nonparametric outlier methods.
result Demonstrates robustness of the method under various levels of drift class contamination.
New CUSUM algorithm detects changes in unnormalized models.
problem Change detection in models with unknown normalization constants.
method Score-based CUSUM (SCUSUM) algorithm based on Fisher divergence and Hyvärinen score.
result Asymptotic optimality of the SCUSUM algorithm demonstrated.
Proposes neural SDEs with change points for better time series modeling.
problem Restrictions in modeling time series with distributional shift.
method Generative adversarial networks (GANs) for SDEs and change point detection.
result Jointly learns change points and SDE model parameters.
This work introduces robust counterfactuals for neural networks that remain valid after minor model changes.
problem Generating counterfactual explanations that are robust to slight model changes.
method Introduces a measure called Stability to quantify robustness of counterfactuals to potential model changes, and proposes practical relaxations.
result Counterfactuals with high Stability remain valid after potential model changes with high probability.
Method tracks change-points in crypto-assets extremes.
problem Tracking change-points in multivariate extremes.
method Statistical method for modeling change-points on crypto-assets extremes.
result Developed a method to track crypto-assets extremes.
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
Paper detects gradual changes in cluster structure using MC fusion.
problem Detecting gradual changes in cluster structure over time.
method MC fusion for multiple mixture numbers, examining MC transition.
result Accurately captures cluster structure during transitional periods.
Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on …
We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…
Detects model changes in data streams using Ddim.
problem Early detection of model changes in data streams.
method Continuous model selection based on Ddim.
result Early warning signals of model changes.
Model change points in time-series data with neural SDEs and variational autoencoders.
problem Modeling change points in time-series data with neural stochastic differential equations.
method Proposes a novel model formulation and training procedure based on the variational autoencoder framework, alternating between updating neural SDE parameters and change points.
result Demonstrates the expressive power of the proposed model in modeling both classical parametric SDEs and real datasets with distribution shifts.
GOCPD detects change points by maximizing the probability of two independent models.
problem Large false discovery rates in online change point detection methods.
method GOCPD uses ternary search to find change points by maximizing the probability of two independent models.
result GOCPD accelerates CPD with logarithmic complexity for single change point detection.
Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model with parameter θ, and a post-change model with parameter θ′, respectively. The goal is to detect whether the change in the model is sign…
New concept of mixture complexity helps detect gradual clustering changes.
problem Determining the number of clusters in mixture models with overlaps and weight biases.
method Introducing mixture complexity (MC) as a new measure of cluster size, defined from information theory.
result MC can detect gradual clustering changes, allowing earlier detection and finer distinction.
We study exponential Levy models with change-point which is a random variable, independent from initial Levy processes. On canonical space with initially enlarged filtration we describe all equivalent martingale measures for change-point model and we give the conditions for the existence of f-divergence minimal equival…
This work introduces a method to attribute model performance drops to distribution shifts.
problem Attributing performance drops of machine learning models to distribution shifts.
method Formulated as a cooperative game, value of a set of distributions is defined as the change in model performance when only that set of distributions changes. Importance weighting method for computing the value of an arbitrary set of distributions is derived. Quantifying the contribution of each distribution as its Shapley value.
result Demonstrated the effectiveness of the method on various case studies.
Carr and Wu (2004), henceforth CW, developed a framework that encompasses almost all of the continuous-time models proposed in the option pricing literature. Their framework hinges on the stopping time property of the time changes. By analyzing the measurability of the time changes with respect to the underlying filtra…
A new method detects changes in machine learning models over time.
problem Automatic monitoring of machine learning models trained on evolving data.
method Score-based statistical hypothesis test for change detection.
result The method can detect changes in any number of model components.
New MIP approach for efficient change-point detection.
problem Offline multiple change-point detection in data streams.
method Mixed-integer programming (MIP) for globally optimal PWL fitting.
result Provable tighter relaxations for segment assignment variables.
Extends Neural ODEs to model discrete changes in continuous systems.
problem Lack of explicit termination time in existing Neural ODE formulations.
method Introduces neural event functions to implicitly define termination criteria.
result Models discrete changes in continuous systems without prior knowledge.
Estimates change point in high-dimensional dynamic graphical models.
problem Detecting change points in high-dimensional graphical models.
method Developed an estimator with Op(ψ−2) rate of convergence, established asymptotic distribution under high-dimensional scaling. result Asymptotic distribution characterized under vanishing and non-vanishing jump size regimes.
Bayesian model detects sudden changes in stock market correlations during pandemic.
problem Capturing sudden structural changes in financial dependence during global events.
method Develops a Bayesian multivariate stochastic volatility model based on time-varying graphs.
result Captures abrupt changes in dependence structure across US stock portfolios.
Robust quickest change detection method for unknown score functions.
problem Detecting changes in data streams with unknown pre- and post-change distributions.
method Selects least-favorable distributions and robustifies score-based detection algorithm.
result Demonstrates improved performance in simulations.
Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce s…
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next cha…
Framework LiLY recovers latent causal variables from time-series data under distribution shifts.
problem Learning and correcting models under unknown distribution shifts in time-series data.
method LiLY framework that recovers latent causal variables and identifies their relations from temporal data under different distribution shifts.
result The framework reliably identifies time-delayed latent causal influences from observed variables under different distribution changes.
Neural-network emulators predict sea-level changes due to Antarctic ice melt.
problem High computational cost and time in projecting sea-level changes.
method Built neural-network emulators of sea-level change using GRD effects from future Antarctic Ice Sheet mass change.
result Neural-network emulators are as accurate as baseline machine learning emulators and offer substantial computational efficiency.
Balancing graph summarization and change detection in streaming data.
problem Balancing compression rate in graph summarization and accuracy in change detection.
method Introducing a probabilistic hierarchical latent variable model and optimizing parameters based on the minimum description length principle to balance the trade-off.
result Guaranteed suppression of Type I error probability (false alarms) in change detection.
Estimates change points in Weibull time series with copulas.
problem Change-point estimation for nonlinear Weibull time series with copula-based Markov models.
method Copula-based Markov chain model with Weibull marginal distributions, incorporating asymmetric dependence structures through Clayton and Joe copulas.
result Proposed method performs well in estimating change points and model parameters, demonstrated through extensive numerical studies and empirical application.
BOIL updates model body only, showing better few-shot learning performance.
problem Few-shot learning efficiency with model reuse vs. change.
method Proposes BOIL, updating only model body, freezing head.
result Significantly outperforms MAML on cross-domain tasks.
New test detects local changes in high-dimensional Gaussian graphical models online.
problem Detecting abrupt changes in high-dimensional Gaussian graphical models in real-time.
method Develops a novel test based on the ℓ∞ norm of the normalized covariance matrix.
result Demonstrates good performance in various experimental settings.
A new method detects change points in time series with conceptors.
problem Detecting change points in time series with nonlinear temporal dependence.
method Use of conceptor matrix to learn baseline dynamics and identify change points.
result The method provides a consistent estimate of the true change point.
A model of fluctuations in the market price including many deterministic dealers, who predict their buying and selling prices from the latest price change, is developed. We show that price changes of the model is approximated by ARCH(1) process. We conclude that predictions of dealers affected by the past price changes…
Method identifies change points in high-dimensional models using sample weights.
problem Identifying change points in high-dimensional generalized linear models.
method Sample-weighted empirical risk minimization (Weighted ERM).
result Weighted ERM yields precise asymptotic performance characterization for Gaussian designs.
The paper develops methods to accurately locate change points in high-dimensional mean shift models.
problem Locating change points in high-dimensional mean shift models.
method Locally refitted least squares estimator, component-wise and simultaneous rates of estimation.
result Asymptotic validity of component-wise and simultaneous confidence intervals for change point parameters.
We study the stability of several no-arbitrage conditions with respect to absolutely continuous, but not necessarily equivalent, changes of measure. We first consider models based on continuous semimartingales and show that no-arbitrage conditions weaker than NA and NFLVR are always stable. Then, in the context of gene…
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to …
Improves change-point detection for high-dimensional time-series.
problem Uncertainty in latent variable estimation affects change-point detection.
method Proposes multinomial sampling to improve detection rate and reduce delay.
result Results outperform baseline method in experiments.
We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…
Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian m…
Graph change-point detection method learns graph similarity from data.
problem Detect abrupt changes in dynamic networks.
method Siamese graph neural network for graph similarity learning.
result Method detects changes in diverse types of networks with minimal data history.
In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified threshold…