Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with relatively small counts are approximated with normal distributions, multiplicative noise approximations are straightforward to implement. Th…
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48 results for “Multiplicative Error Model”
We design a new algorithm for the Euclidean -means problem that operates in the local model of differential privacy. Unlike in the non-private literature, differentially private algorithms for the -means objective incur both additive and multiplicative errors. Our algorithm significantly reduces the additive erro…
Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. It is recognized as a powerful tool in different applied fields such as bioinformatics, error-control codes, speech language, information retrieval and others. Gaussian graphical model selection is a statist…
A new method uses RF's out-of-bag errors for multiple imputation.
problem Missing data in biomedical studies and lack of prediction uncertainty.
method Constructs conditional distributions from the empirical distribution of out-of-bag prediction errors.
result Valid multiple imputation results achieved without parametric assumptions.
Finite-Sample Properties of Model Specification Tests for Multivariate Dynamic Regression Modelsecon.EM
Proposes a new test for validating multivariate dynamic regression models.
problem Inadequate exogeneity conditions for conventional model specification tests in dynamic systems.
method Develops a generalized Durbin estimator for multiple-equation systems with dynamic dependencies, and constructs Wald tests.
result Bootstrap-based Wald tests improve finite-sample size control and validate the null hypothesis in multifactor models.
Develops a framework to quantify uncertainties in multiple ML models.
problem Uncertainty in ML model predictions and model inputs.
method Develops a theoretical framework to decouple and transform uncertainties.
result Generates joint distribution of ML predictions considering uncertainties.
We learn linear models from nonlinear systems using multiple trajectories and regularization.
problem Identifying linear models from data when the underlying dynamics are nonlinear.
method Multiple trajectories data acquisition followed by regularized least squares.
result Learn linearized dynamics with arbitrarily small error given enough samples.
In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …
Calibration without labels in multiple testing
problem Interpretable error probabilities in large-scale hypothesis testing
method Constructing pseudo-labels from spacings of ordered -values
result Finding that -value can be severely miscalibrated
Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are me…
The study improves theoretical understanding of using multiple synthetic datasets for better model accuracy.
problem Lack of theoretical understanding of using multiple synthetic datasets for supervised learning.
method Derive bias-variance decompositions for multiple synthetic datasets settings.
result A simple rule of thumb to select the appropriate number of synthetic datasets.
Proposes MR estimator for unbiased recommendation models.
problem Data biases in recommender systems lead to inaccurate predictions.
method Introduces multiple robust (MR) learning approach combining multiple imputation and propensity models.
result MR estimator achieves unbiasedness when any of the models is accurate.
Nonlinear kernels can be approximated using finite-dimensional feature maps for efficient risk minimization. Due to the inherent trade-off between the dimension of the (mapped) feature space and the approximation accuracy, the key problem is to identify promising (explicit) features leading to a satisfactory out-of-sam…
Max-rank improves multiple testing in conformal prediction.
problem Simultaneous testing of multiple hypotheses in scientific inquiries.
method Introduces max-rank, a novel correction for positive dependencies in simultaneous testing.
result Max-rank efficiently controls family-wise error rate and improves predictive uncertainty estimates.
New PAC-Bayes bound controls multiple error types simultaneously.
problem Current PAC-Bayes bounds are limited to scalar metrics.
method Bounding KL divergence between empirical and true probabilities of multiple error types.
result First PAC-Bayes bound for rich information-rich certificates.
The study corrects measurement error in evaluating health effects of multiple pollutants.
problem Bias in estimating health effects of air pollution constituents due to mismeasurement.
method Used a linear regression calibration model and extended DML approach to correct for measurement error.
result Identified two PM2.5 constituents (Br and Mn) that show a negative causal effect on cognitive function after correction.
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We propose two statistical tests, each controlling a different notion of decision errors. The first test, building on the post selection inference…
New DMEM models forecast volatility combining low- and high-frequency data.
problem Modeling realized volatility with both short- and long-term features.
method Doubly Multiplicative Error (DMEM) models combining daily and long-term data.
result DMEM models outperform existing GARCH-type models in forecasting.
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805.06826). Many of their remarks are interesting. But they also claim that the paper has "foundational errors" and that its "premise is...incorrect." These claims are not substantiated. There are no foundatio…
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower …
MEM models improve volatility forecasting in financial markets.
problem Improving volatility forecasting in financial markets.
method Multiplicative Error Model (MEM) framework for positive-valued time series.
result MEMs parsimoniously produce good forecasts of asset returns.
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of meas…
CB-SLICE identifies concept-based error slices in deep learning models.
problem Systematic errors in deep learning models on specific groups.
method Concept Bottleneck Models (CBMs) and concept representations.
result CB-SLICE outperforms state-of-the-art methods in error slice identification.
An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we stud…
New framework identifies and reduces errors in machine learning under distribution shift.
problem Errors in machine learning models when distributions change.
method Developed a principled framework to characterize and eliminate epistemic errors in imperfect multitask learning.
result Provided a decompositional epistemic error bound for general settings of distribution shift.
Method estimates LLM error rates using Pareto optimization.
problem Quantifying error rates in text-generating models.
method Pareto optimization for generating risk scores.
result Risk scores correlate well with true error rates.
Proposes a new SPVM model for RVM with more flexible priors.
problem Improper priors on multiple penalty parameters in RVM lead to improper posteriors.
method Introduces a single penalty approach (SPRVM) and a semi-Bayesian fitting method.
result SPRVM allows for more flexible priors and has proven conditions for posterior propriety.
Pareto Testing optimizes model performance under multiple constraints.
problem Optimizing machine learning models with multiple conflicting objectives.
method Two-stage process combining optimization and statistical testing.
result Models can be configured to satisfy multiple statistical guarantees and objectives.
Analyzes error sources in global feature effect estimation methods.
problem Unexplored error sources in global feature effect estimation methods.
method Systematic, estimator-level analysis of bias and variance.
result Holdout data is theoretically cleanest, but estimation variance depends on sample size and model characteristics.
TCE measures calibration error with a test-based approach.
problem Measuring calibration error of probabilistic binary classifiers.
method TCE uses a novel loss function based on a statistical test.
result TCE offers clear interpretation, consistent scale, and enhanced visual representation.
The paper introduces a method to model error correlations in multivariate time series forecasting.
problem Accurate modeling of error correlations for reliable uncertainty quantification.
method Plug-and-play method that learns error covariance over multiple steps using low-rank-plus-diagonal and independent latent temporal processes.
result Improves predictive accuracy and uncertainty quantification without significantly increasing parameter size.
New insights into training machine learning models with momentum.
problem Lack of theoretical understanding on the generalization error of momentum-based methods.
method Analyzed modified momentum-based update rule (SGDEM) for smooth Lipschitz loss functions.
result SGDEM admits an upper-bound on the generalization error for smooth Lipschitz loss functions.
This work studies scaling laws for low-precision training in high-dimensional linear regression.
problem Optimizing trade-off between model quality and training costs in high-dimensional linear regression.
method Theoretical study of scaling laws for low-precision training within a high-dimensional sketched linear regression framework, analyzing multiplicative and additive quantization.
result Multiplicative quantization maintains full-precision model size, while additive quantization reduces effective model size.
SGMs are robust to practical errors via uncertainty quantification.
problem Robustness of SGMs to practical implementation errors.
method Wasserstein uncertainty propagation (WUP) theorem and Bernstein estimates.
result SGMs are provably robust to multiple sources of error.
Error-robust multi-view clustering tackles noisy data across multiple sources.
problem Error in multi-view data degrades clustering performance.
method Blind clustering without error consideration is ineffective. Various approaches like sparsity, graph, subspace, and deep learning are reviewed.
result Error-robust multi-view clustering improves clustering accuracy even with corrupted data.
Estimates multiple linear systems on a graph with smoothness constraints.
problem Joint estimation of multiple linear systems under graph smoothness constraints.
method Proposes estimators for joint estimation of system matrices with error bounds.
result MSE converges to zero as increases, typically polynomially fast w.r.t .
The study classifies policy announcements' impact on stock market volatility.
problem Evaluating the impact of Central Bank announcements on stock market volatility.
method Proposed a model-based classification method using Markov Switching dynamics and Multiplicative Error Model.
result Successful classification of 144 European Central Bank announcements on stock market volatility.
The study examines how equivariance in networks affects generalization error using PAC-Bayesian bounds.
problem Understanding how equivariance in networks impacts generalization error.
method Utilized PAC-Bayesian analysis for equivariant networks, deriving norm-based bounds for generalization error.
result The bound indicates that using larger group size in the model improves generalization error.
New estimator optimizes black-box model errors in semiparametric estimation.
problem How nuisance estimation errors affect low-dimensional target parameters in semiparametric models.
method Proposed a new estimator achieving a sharper rate of convergence.
result The first-order stochastic error of nuisance estimation can be eliminated.
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesstat.ML
A latent force model is a Gaussian process with a covariance function inspired by a differential operator. Such covariance function is obtained by performing convolution integrals between Green's functions associated to the differential operators, and covariance functions associated to latent functions. In the classica…
The paper applies communication theory to improve language model reranking.
problem Ensuring safety and accuracy in language model outputs.
method Drawing parallels between communication theory and language model reranking, the authors propose a protocol to improve reliability.
result The proposed protocol can achieve asymptotically error-free performance in noisy communication scenarios.
Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative appro…
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a state and an action and outputs the next state---a one-step model. This model can be composed with itse…
The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entai…
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
The paper proposes a new system ID method from noisy data.
problem System identification of linear and nonlinear non-autonomous systems from noisy and sparse data.
method Bayesian formulation for learning a hidden Markov model with stochastic dynamics, analyzed in the context of least squares and multiple shooting approaches.
result The proposed approach outperforms existing methods in terms of mean squared error and model generalizability.
This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.
problem Estimating shared subspace across multiple datasets with varying degrees of misalignment.
method Angle-based Joint and Individual Variation Explained (AJIVE) method, a two-stage spectral approach.
result AJIVE's performance in high signal-to-noise ratio (SNR) regimes and its non-diminishing error in low-SNR settings.