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 , and a post-change model with parameter , respectively. The goal is to detect whether the change in the model is sign…
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48 results for “Parameter Change”
New algorithm detects changes quickly without knowing parameters, near optimally.
problem Quickest change detection with unknown parameters.
method Leverages theoretical asymptotic properties to derive a scalable approximate algorithm with near optimal performance.
result Detects changes in constant complexity with near optimal performance.
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, whi…
Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.
problem Detecting change-points and estimating parameters in nonlinear dynamical systems with regime transitions.
method Residual-loss anomaly analysis of physics-informed neural networks, two-stage strategy.
result The method outperforms traditional approaches in change-point localization and parameter estimation accuracy.
Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, medicine and biology. In these domains, the size of the population under study is often very large, however, the number of observations availa…
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.
Estimates change point in high-dimensional dynamic graphical models.
problem Detecting change points in high-dimensional graphical models.
method Developed an estimator with rate of convergence, established asymptotic distribution under high-dimensional scaling.
result Asymptotic distribution characterized under vanishing and non-vanishing jump size regimes.
Inference on the change point in high dimensional time series models via plug in least squaresstat.ME
Estimates change point in high dimensional time series models.
problem Change point estimation in high dimensional time series.
method Plug-in least squares estimator with sufficient conditions for adaptivity.
result Optimal rate of convergence in integer scale.
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.
Bayesian method detects change points in time series data.
problem Detecting significant regime shifts in time series data.
method Bayesian autoregressive model with time-varying parameters.
result Enhanced estimate accuracy and forecasting power.
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.
Optimal search for change point anomaly in multiple processes.
problem Detecting a change point in an anomalous process among multiple normal processes.
method Deterministic search algorithm balancing sample complexity and detection accuracy.
result Asymptotically optimal in minimizing Bayes risk.
CDSSD detects sparse changes in partially observable data streams.
problem Online change detection of sparse changes in partially observable high-dimensional data streams.
method Smooth-sparse decomposition, spike-slab variational Bayesian inference, adaptive sampling via Thompson sampling.
result CDSSD effectively detects sparse changes in partially observable data streams.
Novel optimization method detects change points in Gaussian data.
problem Detecting change points in univariate Gaussian data sequences.
method Continuous optimization for best subset selection (COMBSS) applied to a reformulated statistical inverse problem.
result Adaptation and evaluation of COMBSS for offline normal mean multiple change-point detection.
Cross-validation pitfalls in change-point regression are addressed with new approaches.
problem Cross-validation's prediction error-based criterion may lead to under- or over-estimation of change-points.
method Proposes two approaches: absolute error loss and modified holdout sets.
result Consistent estimation of the number of change-points under certain conditions.
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity is defined by the Hellinger distance between distributions before…
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change-point algorithm is used to identify the time indices (change points) at which the system transitions …
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
problem Inaccurate pricing of energy contracts using the Black-Scholes-Merton model.
method Integrates regime switching and time-changed Levy processes with a two-state Markov chain.
result Improved accuracy in pricing energy contracts through a new model.
Proposes a deep learning method for modeling dynamic individual-level latent trajectories with changing parameters.
problem Modeling longitudinal data with changing individual-level dynamics parameters.
method Combines deep learning for dimensionality reduction and differential equations for dynamic modeling, allowing different parameters for sub-periods.
result Successfully identifies dynamic parameters and predictors of resilience.
We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to modify the original optimization problem by adding K independent Gaussian priors (corresponding to the k-means objective) over the network p…
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Seriescs.LG
Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait …
We consider the problem of estimating the location of a single change point in a dynamic stochastic block model. We propose two methods of estimating the change point, together with the model parameters. The first employs a least squares criterion function and takes into consideration the full structure of the stochast…
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matchingstat.ML
Paper efficiently infers differential parameters in time-varying models using time score matching.
problem Efficiently inferring differential parameters in time-varying probabilistic models.
method Directly estimates the differential parameter using time score matching and proves consistency of the method.
result Consistent estimation of parameter derivatives in high-dimensional settings.
The problem of change-point estimation is considered under a general framework where the data are generated by unknown stationary ergodic process distributions. In this context, the consistent estimation of the number of change-points is provably impossible. However, it is shown that a consistent clustering method may …
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…
Alexander polynomial condition blocks crossing changes in some knots.
problem Cosmetic crossing changes in knot diagrams.
method Alexander polynomial obstruction for -space knots.
result Proved the conjecture for a five-parameter family of pretzel knots.
Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…
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 introduce a class of randomly time-changed fast mean-reverting stochastic volatility models and, using spectral theory and singular perturbation techniques, we derive an approximation for the prices of European options in this setting. Three examples of random time-changes are provided and the implied volatility sur…
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…
Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into …
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as SGD. A relaxation of the IID condition is a probabilistic symmetry known as exchangeability. We show the sense in …
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
problem Non-cooperative games with globally uncertain parameters and no common prior.
method Mixed strategies and subjective priors, Extended Equilibrium defined by fixed-point argument.
result Existence of Extended Equilibrium under certain conditions.
An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time V…
This paper proposes a new method to adapt ROMs for new parameter settings.
problem ROMs lack robustness when applied to new parameter settings.
method Regression trees on Grassmann Manifold to learn the mapping between parameters and POD bases.
result The proposed method is capable of establishing the mapping between parameters and POD bases, thus adapting ROMs for new parameters.
New theory controls compression change probability without prior knowledge.
problem Controlling compression change probability without prior knowledge.
method New theory on compression function and statistical risk.
result Cardinality of compressed set is a consistent estimator of probability of change of compression.
To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but tha…
Inference-Based Quantum Sensingquant-ph
New method infers unknown parameters in quantum sensing with high probability.
problem Estimating unknown parameters in quantum sensing with limited measurements.
method Inference-based scheme for characterizing system response at 2n+1 parameters.
result Inference error is smaller than δ with a number of shots scaling as Ω(log^3(n)/δ^2).
We examine the issue of sensitivity with respect to model parameters for the problem of utility maximization from final wealth in an incomplete Samuelson model and mainly, but not exclusively, for utility functions of positive power-type. The method consists in moving the parameters through change of measure, which we …
New algorithm for nonstationary multi-armed bandits with optimal performance.
problem Nonstationary multi-armed bandits with changing model parameters over time.
method Adaptive Resetting Bandit (ADR-bandit) algorithm using adaptive windowing techniques.
result ADR-bandit achieves nearly optimal performance in both abrupt and gradual changes.
Deep Importance Samplingq-fin.CP
A new method reduces Monte Carlo variance for financial payoffs.
problem Reducing variance in Monte Carlo estimators for financial payoffs.
method Path-dependent importance sampling using neural networks.
result Significant variance reduction (2-9 times) for various financial payoffs.
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…
Rate change calculations in the literature involve deterministic methods that measure the change in premium for a given policy. The definition of rate change as a statistical parameter is proposed to address the stochastic nature of the premium charged for a policy. It promotes the idea that rate change is a property o…
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…