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.
CONDA-PM framework helps analyze concept drift in business processes.
problem Analyzing changes in business processes over time.
method Systematic Literature Review and framework development.
result Highlights areas needing research to complement existing efforts.
Paper introduces a method to explain concept drift using counterfactual explanations.
problem Understanding the features where concept drift occurs for better model adjustment.
method Formal definition and algorithm based on counterfactual explanations.
result Demonstrates usefulness of the method in various examples.
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.
problem Handling concept drift in evolving data streams.
method DiwE measures diversity based on regional distribution disagreement and uses it to weight instances and select classifiers.
result DiwE outperforms other algorithms on various synthetic and real-world data stream benchmarks.
Approach to detect and adapt to concept drift in unlabeled streaming data.
problem Detect and adapt to concept drift in high-dimensional, noisy, low-context data.
method Density-based clustering for virtual drift and weak supervision for real drift.
result 90% precision in detecting and adapting to concept drift for 4 years after initial deployment.
Paper proposes a semi-supervised method for detecting concept drift in streaming environments.
problem Detecting concept drift in streaming environments with limited labeled data.
method Utilizes density estimation of posterior probabilities in partially labeled streaming data.
result Demonstrates superior concept drift detection in streaming environments with limited labeled data.
New framework for detecting data drift in continuous time.
problem Drift in data distribution over time.
method Probability theoretical framework for continuous time drift.
result New efficient drift detection method and decomposition of data.
CURIE uses cellular automata to detect concept drift in data streams.
problem Detecting changes in data distribution (concept drift) in data streams.
method CURIE represents data stream distribution in a cellular automata grid and uses its neighborhood rule to detect changes.
result CURIE, when hybridized with base learners, performs competitively in detection metrics and classification accuracy.
Detects drifts in data for classification tasks using constrained embeddings.
problem Drifts in data affect model performance; unsupervised methods ignore label information.
method Task-sensitive semi-supervised drift detection with constrained low-dimensional embedding.
result Successfully detects real drifts affecting classification performance.
PDD detects concept drift using explainable AI, improving model performance in dynamic environments.
problem Detecting and adapting to concept drift in predictive models.
method Profile Drift Detection (PDD) using Partial Dependence Profiles (PDPs).
result PDD outperforms existing methods in detecting concept drift and maintaining high predictive performance.
Paper proposes a beta distribution method for detecting concept drift in adaptive classifiers.
problem Adaptive classifiers need to detect and respond to concept drift in real-time data streams.
method The paper introduces a beta distribution model to monitor model error and identify abnormal behavior as drift.
result The method effectively detects abrupt changes in model error, improving classifier performance.
Transform drift of diffusions without knowing if measure change is a martingale.
problem Transforming drift in diffusions without knowing if measure change is a martingale.
method Characterize when measure change local martingale is a true martingale.
result Complete characterization of measure change local martingale being a true martingale.
New framework detects adversarial concept drift in streaming data.
problem Adversarial concept drift in dynamic environments.
method Predict-Detect streaming framework for unsupervised drift detection and recovery.
result Framework detects adversarial drift with <6% labeled data, improving active learning for imbalanced data.
Paper presents adaptive minimax risk classifiers for multidimensional concept drift.
problem Multidimensional concept drift in supervised classification.
method Adaptive minimax risk classifiers (AMRCs) tracking multivariate and high-order distribution changes.
result AMRCs provide computable tight performance guarantees and improve classification.
A new drift detection method based on autoregressive models.
problem Concept drift in real-world data leads to decreased model performance.
method Autoregressive based drift detection method (ADDM).
result ADDM outperforms state-of-the-art drift detection methods.
Bayesian method adapts to unknown distribution shifts in online learning.
problem Online learning with unknown and irregular distribution shifts.
method Bayesian inference with change-point detection and beam search.
result Improves adaptation to new data distributions over state-of-the-art methods.
New algorithm learns changing discrete distributions with minimal drift error.
problem Learning discrete distributions that change over time with limited past samples.
method Adaptive algorithm using data-dependent bounds to balance statistical and drift errors.
result Tighter statistical error bounds for drifting distributions with or without finite support.
A new method detects concept drift in streaming data using k-means space partitioning.
problem Detecting distribution changes in streaming data.
method Equal intensity k-means space partitioning (EI-kMeans) and heuristic sensitivity improvement.
result EI-kMeans improves drift detection accuracy and sensitivity.
This paper solves a Bayes sequential impulse control problem for a diffusion, whose drift has an unobservable parameter with a change point. The partially-observed problem is reformulated into one with full observations, via a change of probability measure which removes the drift. The optimal impulse controls can be ex…
New algorithm detects concept drifts from unlabeled data streams.
problem Detecting concept drifts from unlabeled data streams.
method Margin Density Drift Detection (MD3) algorithm.
result MD3 reliably detects drifts with fewer false alarms.
Machine learning models fail due to concept and data drift during pandemic.
problem Machine learning models trained before the pandemic are unreliable during the pandemic.
method Detect and diagnose concept and data drift in models.
result Model resilience and robustness are crucial for future predictions.
A framework monitors and diagnoses concept drift in supervised learning models.
problem Changes in predictive relationships over time render models suboptimal.
method Score vector monitoring using exponentially weighted moving average.
result Score-based approach detects concept drift more effectively than error-based methods.
Proposes a self-learning algorithm using reinforcement learning to adapt to data changes.
problem Overcoming concept drift in statistical models over time.
method Reinforcement learning combined with genetic algorithms and Q-learning.
result Algorithm can adapt to new data patterns and self-calibrate.
A new framework detects concept drift in streaming data.
problem Detecting distributional changes in non-stationary data streams.
method Treating model parameters as random variables, ERICS uses information theory measures to identify concept drift.
result ERICS effectively detects concept drift compared to existing methods.
New approach handles changing data distributions via reused models.
problem Handling concept drift in streaming data.
method Model reuse with adaptive weights based on performance.
result Adaptive model weights improve performance on various datasets.
A framework for evaluating and benchmarking concept drift detection methods
problem Data stream mining challenged by concept drift
method A novel benchmarking framework
result Fair comparisons of drift detection methods
ProteuS generates synthetic financial data with regime changes for testing drift detection.
problem Simulating concept drift in financial markets for model evaluation.
method ARMA-GARCH models fitted to ETF data, generating synthetic time series with predefined regime changes.
result Generated datasets reveal the complexity of detecting and adapting to market regime changes.
Machine learning monitors detect motor overheating, adapting to concept drift.
problem Early detection of motor overheating in ships' propulsion systems.
method Machine learning and statistical methods using historical data to adapt to concept drift.
result The proposed monitors provide early detection of overheating during and after concept drifts.
Develops PromptShift-CRC for drift-aware conformal risk control in foundation models under prompt and domain shift.
problem Fixed calibration risk in foundation models due to prompt and domain shift.
method Embeds prompts and responses, measures drift, gives more weight to recent examples, and updates risk online.
result Develops method to control risk up to terms for distribution mismatch and weighted quantile uncertainty.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
New method detects drift in high-dimensional data.
problem Understanding and localizing concept drift in learning systems.
method Conformal predictions for drift localization.
result Our approach outperforms existing methods on image datasets.
Identifies features most relevant to concept drift in data.
problem Identifying features most relevant to concept drift.
method Distinguishing between drift inducing and faithfully drifting features; deriving minimal subsets of features to characterize drift.
result Derives a detection algorithm for concept drift.
Paper proposes a framework to detect adversarial concept drifts under poisoning attacks.
problem Adversarial concept drift in data streams.
method Augmented Restricted Boltzmann Machine with improved gradient computation and energy function.
result High robustness and efficacy of the proposed drift detection framework in adversarial scenarios.
Optimal exercise timing of stock options analyzed with varying information on drift change.
problem Analyzing optimal exercise timing of stock options with varying information on drift change.
method Rigorous mathematical analysis and numerical methods to solve optimal stopping problems.
result Characterization of optimal exercise boundaries and smooth pasting properties in both information scenarios.
Study non-stationary distributions, proving risk bounds for density estimation.
problem Estimating current distribution under gradual changes.
method Proves tight minimax risk bounds for nonparametric density estimation under drift.
result Generalizes previous results on agnostic learning under drift.
The study examines how posterior drift affects forecasting accuracy in overparametrized models, particularly in financial markets.
problem Impact of posterior drift on out-of-sample forecasting accuracy in overparametrized models.
method Investigation of posterior drift and its effect on model performance in financial markets.
result Overparametrized models can be sensitive to sub-periods and bandwidth parameters, leading to inconsistent returns.
CDLEEDS detects local changes in evolving data streams for accurate feature attributions.
problem Local feature attributions become obsolete in evolving data streams.
method CDLEEDS, a flexible framework for detecting local change and concept drift.
result CDLEEDS reliably detects both local and global concept drift.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
KT models struggle with student concept drift, but BKT remains the most stable.
problem Impact of student concept drift on KT models.
method Applied four KT models to five academic years of data.
result KT models generally degrade in performance with concept drift, BKT remains stable.
Unified techniques improve stability and replicability in changing data.
problem Concept drift in data generating distribution.
method Removing hidden confounding and causal regularization.
result Improves stability, replicability, and robustness in heterogeneous data.
A new geometric metric identifies true data changes from parametrization artifacts in high-dimensional representations.
problem Quantifying representation drift in high-dimensional data using Euclidean or cosine distances can misattribute changes due to arbitrary parametrizations.
method Introducing the Fubini Study metric to identify representations that differ only by gauge transformations.
result The Fubini Study metric isolates intrinsic evolution by remaining invariant under gauge-induced fluctuations, providing a diagnostic for meaningful structural changes.
Algorithm detects concept drift and adapts models in streaming data.
problem Concept drift in streaming data renders models inaccurate.
method Adaptive learning algorithm that detects drifts and reacts to them.
result Risk competitive to an algorithm with perfect drift knowledge.
AutoML tackles evolving data with drift detection.
problem Handling changing data distributions in AutoML.
method Extended Auto-Sklearn with drift detection mechanisms.
result Demonstrated effectiveness of the proposed methodology.
New method detects concept drift in data streams with missing values.
problem Uncertainty introduced by missing values in concept drift detection.
method Fuzzy distance estimation and histogram bin allocation.
result Fuzzy set theory improves drift detection in data with missing values.
Paper proposes a method to automatically detect drift in machine learning models.
problem Detecting changes in class-label data distributions that affect model predictions.
method Self-evaluating predictive model degradation to detect concept drift.
result Effectiveness in automatically detecting and describing concept drift.
MDDM detects concept drifts in evolving data streams using McDiarmid's inequality.
problem Concept drift in rapidly evolving data streams causes inaccurate models.
method Sliding window with weights, comparing weighted means over time.
result MDDM detects concept drifts with shorter delays and lower false negatives.
The paper proposes a method for distribution-free prediction sets that adapt to unknown temporal changes.
problem Distribution-free prediction sets require reliable calibration data, which is often unavailable in real-world settings with temporal changes.
method The method selects an adaptive window to construct prediction sets, optimizing a bias-variance tradeoff.
result The method provides sharp coverage guarantees and is shown to be adaptive to temporal drift through numerical experiments.