Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Bayesian Online Changepoint Detection
21 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.
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representative citing papers
BAPR combines Bayesian change detection with robust RL, proves the core operator is a contraction via Lean 4, and adapts conservatism after detected regime shifts in continuous control.
Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.
CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.
A three-regime causal model with a latent build-up phase enables a MAX-aggregation trigger detector to deliver positive expected lead time before observable stress in limit order books.
SKIP achieves 4.16x faster dense video rollouts for robot world models by synthesizing only multimodal-identified keyframes and interpolating the rest, preserving policy training effectiveness with minimal success rate drops.
SWORD detects change points in dynamic graphs by averaging Chebyshev moments of the normalized Laplacian over two time windows and using L1 distance, improving mean F1 from 0.27 to 0.79 over prior spectral methods on real benchmarks.
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
The paper introduces a path signature-based method for detecting creative fatigue in digital ads using geometric change detection on performance paths.
BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
Pi-Change extends the PELT framework for multiple change point detection by incorporating prior information on locations through a time-varying penalty that preserves dynamic programming efficiency.
Posterior learning debt enables cost-sensitive retraining decisions that outperform calendar-based and CUSUM methods in synthetic Bayesian simulations.
Heavy-tailed continuous HMMs recover volatility clustering and produce regime-conditional VaR that passes joint conditional coverage tests on US equity data.
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.
LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.
A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.
A Bayesian method clusters time series by similarity in the timing of their most recent volatility change-points via a metric on posterior distributions, demonstrated on S&P 500 returns.
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