In this paper, we derive Hybrid, Bayesian and Marginalized Cramér-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compr…
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48 results for “Bayesian MSE”
Optimizes sparse signal recovery using nonlinear approximations.
problem Recovering sparse stochastic signals efficiently.
method Probabilistic approach with linear and nonlinear estimators.
result Structured estimator outperforms linear in MSE.
SA-BCP combines long-term and local evidence for efficient, adaptive online prediction.
problem Balancing fast adaptation and stable coverage in online prediction.
method State-Adaptive Bayesian Conformal Prediction (SA-BCP) using gated convex combination of temporal inertia and spatial evidence.
result SA-BCP achieves at-or-above-nominal coverage with substantially sharper intervals compared to discounted Bayesian CP.
Enhances ENet's prediction accuracy while maintaining uncertainty estimation.
problem Gradient shrinkage problem in ENet's loss function.
method Proposes a multi-task learning framework with a modified MSE loss function.
result Improves ENet's prediction accuracy without losing uncertainty estimation.
Bayesian optimization improves forest inventory sampling using remote sensing data.
problem Optimizing forest inventory sampling in large areas with limited data.
method Bayesian optimization applied to RS data for improved sampling design.
result The proposed method outperforms baseline methods in terms of MSE values.
Study shows MSE with sigmoid can match SCE in classification tasks, especially with noisy data.
problem Inconsistent errors in neural network classification tasks.
method Introduced Output Reset algorithm to use MSE with sigmoid activation.
result MSE with sigmoid activation achieves comparable accuracy and convergence rates to Softmax Cross-Entropy, especially in noisy data scenarios.
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Errorcs.LG
We derive a mapping between MSE and CCC, revealing counterintuitive insights.
problem Missing mapping between mean square error and concordance correlation coefficient.
method Derive mathematical formula connecting MSE and CCC, analyze graphical implications.
result Formula uncovers counterintuitive insights and precise range for CCC given MSE.
Deep nets trained with MSE loss exhibit Neural Collapse, collapsing features and classifiers to class means.
problem Understanding Neural Collapse in MSE-trained deep nets.
method Developed a new MSE loss decomposition and introduced the central path concept.
result Exact dynamics of Neural Collapse along the central path can be predicted.
This article presents valuation of Treasury Bonds (T-Bonds) on Macedonian Stock Exchange (MSE) and empirical test of duration, modified duration and convexity of the T-bonds at MSE in order to determine sensitivity of bonds prices on interest rate changes. The main goal of this study is to determine how standard valuat…
Gaussian surrogates improve Poisson imaging performance at low doses.
problem Improving Poisson imaging performance at low doses.
method Analysis of Poisson and Gaussian surrogate reconstruction objectives under Poisson noise.
result Gaussian surrogates can achieve MSE comparable to Poisson MAP at low doses.
This work justifies neural collapse under MSE loss and analyzes the optimization landscape.
problem Understanding neural collapse in deep neural networks under MSE loss.
method Global landscape analysis of vanilla nonconvex MSE loss.
result The only global minimizers are neural collapse solutions.
New estimators outperform maximum likelihood without hyper-parameter estimation.
problem Improving system identification performance without hyper-parameter estimation.
method Developed generalized Bayes and closed-form biased estimators using excess MSE.
result New estimators have comparable performance to empirical-Bayes-based regularized estimator.
Deep learning detects microsleep episodes in EEG data.
problem Automatic detection of microsleep episodes (MSEs) in EEG data.
method Convolutional neural networks (CNNs) and LSTM networks were implemented to analyze MWT data.
result Deep learning algorithms showed good performance close to human experts in detecting MSEs.
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Usersq-fin.RM
Paper proposes HIDAM model to improve MSE default risk assessment using heterogeneous information networks.
problem Default risk assessment for MSEs due to lack of credit information and diverse financial activities.
method HIDAM model incorporating heterogeneous information networks with multi-typed nodes and links, extracting interactive information through meta-paths, and using a hierarchical attention mechanism.
result HIDAM model outperforms state-of-the-art competitors on real-world banking data.
LEXO improves changepoint detection stability and accuracy with backward lags.
problem Stable changepoint detection in sequential data.
method LEXO incorporates backward lags into EXO's forward recursive message-passing algorithm for more stable detection.
result Parameter estimates from LEXO have lower MSE than EXO.
We consider the problem of subspace estimation in a Bayesian setting. Since we are operating in the Grassmann manifold, the usual approach which consists of minimizing the mean square error (MSE) between the true subspace and its estimate may not be adequate as the MSE is not the natural metric in the Gra…
Noise in imputed values corrects biases in machine learning models.
problem Systematic biases in imputed values affect downstream analyses.
method Introducing noise to imputed values to correct biases.
result Noise-corrected imputation methods produce unbiased estimates.
Proposes new terms for neural image compression to improve quality and efficiency.
problem Improving the quality and efficiency of neural image compression.
method Introduces a compression objective and a cycle loss term, applied to autoencoder encoder outputs, combined with reconstruction losses.
result Different autoencoders trained with varying losses produce images with distinct perceptual qualities and image-domain distortions.
This paper solves hedging in incomplete markets using neural networks.
problem Hedging in incomplete markets with risk factor, illiquidity, and discrete transaction dates.
method Proposes a jump-diffusion model and uses RNN, LSTM, and Mogrifier-LSTM neural networks for hedging strategies.
result Mogrifier-LSTM is the fastest and most effective model for hedging.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxationstat.ML
New method for efficient graph signal sampling and reconstruction.
problem Minimizing MSE in graph signal reconstruction with noisy data.
method Formulated as binary constraint minimization, approximated via SDP relaxation and greedy algorithm.
result Randomized greedy algorithm provides near-optimal subset with significant speedup.
Bagging reduces MSE for unstable estimators like trees, but only if the distribution has high kurtosis.
problem Theoretical properties of bagging applied to statistical estimators.
method Theoretical analysis of MSE reduction for bagged estimators, focusing on variance estimation.
result Bagging reduces MSE for unstable estimators like trees, but only if the distribution has high kurtosis.
CAEL-MIPS learns embeddings to improve MIPS for better OPE in contextual bandits.
problem High variance in IPS weighting for OPE in large action spaces.
method Context-Action Embedding Learning (CAEL) for MIPS to minimize MSE.
result CAEL-MIPS outperforms baselines in MSE for OPE in contextual bandits.
The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.
problem Understanding why history-dependent policies can improve MSE in off-policy evaluation.
method The paper derives a bias-variance decomposition of MSE for various OPE estimators, showing how history-dependent policies can decrease variance and increase bias.
result History-dependent policies can decrease the variance of importance sampling estimators, leading to lower MSE.
A new KF handles outliers without MSE loss.
problem Outliers degrade Kalman filter performance.
method NUV priors, EM and AM for variance estimation.
result Outlier-insensitive KF outperforms existing methods.
Replacing MSE with f-divergence in diffusion models improves robustness under data contamination.
problem Improving robustness of diffusion models under data contamination.
method Replacing MSE with f-divergence in diffusion models.
result Empirical improvement in performance under data contamination.
The paper explores MAE as a loss function for DNN vector-to-vector regression, proving its advantages over MSE.
problem Improving loss function for deep neural network based vector-to-vector regression.
method Presenting performance bounds and new properties of MAE, deriving generalized upper bounds, and interpreting MAE as a Laplacian distribution.
result MAE is a more suitable loss function than MSE for DNN based vector-to-vector regression, especially when errors follow a Laplacian distribution.
Robust Synthetic Controlecon.EM
Robust synthetic control method improves comparative case studies.
problem Comparative case studies with missing data and noisy covariates.
method Singular value thresholding to de-noise data, automatic donor selection, robustness to missing data.
result Improved prediction accuracy and robustness to missing data.
Study on LMMSE estimation with model mismatch, quantifying MSE trade-offs.
problem Model mismatch in LMMSE estimation with undermodeling.
method Analyzing the average MSE of LMMSE estimation with random regressors.
result Performance improvement depends on sufficient samples and model complexity.
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
This paper derives lower bounds on the worst case MSE of dictionary learning schemes.
problem Learning a dictionary matrix from observed signals with a common underlying dictionary.
method Information-theoretic approach to minimax estimation for DL problem.
result Derives three lower bounds on the worst case MSE of DL schemes.
Estimates expected information gain using density approximations and dimension reduction.
problem Estimating expected information gain in nonlinear and non-Gaussian settings.
method Flexible transport-based schemes for EIG estimation, optimal sample allocation, and gradient-based upper bounds on mutual information.
result Optimal sample allocation and dimension reduction schemes improve EIG estimation accuracy and convergence rate.
Improved covariance matrix estimation for multiple classes with limited data.
problem Estimating covariance matrices for multiple classes with scarce data.
method Coupled regularized sample covariance matrix estimator (RSCM) that combines pooled SCM and scaled identity matrix for regularization.
result The coupled RSCM estimators outperform cross-validation in classification tasks with comparable accuracy but faster computation.
Paper proposes a new autoencoder metric for balanced learning in imbalanced tabular datasets.
problem Challenges of imbalanced self-supervised learning in tabular data.
method Developed a Multi-Supervised Balanced MSE metric to balance learning.
result The new metric outperforms standard MSE in imbalanced datasets.
A new MI estimator reduces complexity to linear time, achieving optimal MSE rates.
problem High computational complexity of MI estimators.
method Ensemble Dependency Graph Estimator (EDGE) combining LSH, dependency graphs, and ensemble bias-reduction.
result EDGE achieves optimal computational complexity and parametric MSE rate .
Study predicts MSE survival using feature selection with missing data imputation methods.
problem Predicting survival of micro and small businesses (MSE).
method Feature selection with missing data imputation methods (MI, KNN, EM). Comparison of data mining techniques (logistic regression, naive Bayes, LDA, SVM).
result Developed a model to predict MSE survival.
Optimized AIS scheme reduces bias and MSE for general proposals.
problem Performing Monte Carlo integration with general proposals.
method Global optimization of χ²-divergence using stochastic gradient Langevin dynamics.
result Explicit theoretical guarantees for uniform-in-time MSE reduction.
Modern CATE models often fail to outperform a trivial zero-effect predictor, highlighting significant challenges.
problem Lack of robustness in CATE models when applied to real-world data.
method Large-scale benchmark study using diverse observational sampling strategies and novel statistics.
result 62% of CATE estimates have higher MSE than a trivial zero-effect predictor, indicating poor performance.
Bayesian deep learning counts crowds robustly despite occlusions and scale variations.
problem Accurately counting individuals in crowded scenes with occlusions and varying sizes.
method Proposes a Bayesian multi-scale neural network with a ResNet feature extractor, dilated convolutions, and a Perspective-aware Aggregation Module.
result Achieves superior performance on crowd counting benchmarks with uncertainty estimates.
The paper analyzes a simple neural network model with algebraic methods.
problem Finding minima of a ridge-regularized mean squared error for ReLU perceptrons.
method Developed a Divide-Enumerate-Merge strategy using computational algebra.
result Identifies both isolated and connected minima of the RR-MSE.
BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoningcs.LG
BASIS improves LLM reasoning by sharing batchwise rollout info, reducing MSE by 69%.
problem Improving large language model reasoning with limited rollouts and batch information.
method BASIS samples only one rollout per prompt but uses batch information to improve value function estimation.
result BASIS reduces MSE in value function estimation by 69% compared to REINFORCE++.
New analysis reveals optimal regularization for ESNs, avoiding double descent.
problem Characterizing and optimizing Echo State Networks (ESNs) for precise bias-variance.
method Random matrix theory applied to ESNs in a teacher-student setting.
result ESNs achieve lower MSE with limited training samples and teacher memory.
The paper studies SG-MCMC with stale gradients in distributed systems.
problem The impact of stale gradients on convergence properties of SG-MCMC in distributed systems.
method Developed theoretical analysis to show that while bias and MSE depend on staleness, estimation variance remains independent of staleness.
result SG-MCMC with stale gradients can achieve linear speedup in estimation variance with more workers.
Paper improves feature selection accuracy using transfer learning.
problem Improving feature selection accuracy in information criteria-based methods.
method Proposes TLCp, a transfer learning procedure based on Mallows' Cp.
result TLCp outperforms conventional Cp in accuracy and stability.
Paper introduces a new cost function to improve deep learning model generalization.
problem Overfitting and poor extrapolation of deep learning models.
method Introduces a 'whitening' cost function based on the Ljung-Box statistic.
result Significant improvement in generalization for RNNs and image autoencoders.
New method finds best arm minimizing mean-squared error in correlated bandits.
problem Finding an arm that best captures information about other arms.
method Formulated correlated bandit problem, derived MSE estimator, proposed algorithm.
result Proposed algorithm identifies best arm with error probability bounds.
Neural network predicts intelligence from brain structure measurements.
problem Predicting intelligence scores from brain structure measurements.
method Four-layer fully-connected neural network (FNN) using volumes, WM/GM contrast, and cortical thickness.
result Achieved MSE of 94.0270 in test set.
Proposes an efficient shrinkage path for ridge regression.
problem Ill-conditioned data in linear models.
method A new generalized ridge regression shrinkage path that minimizes MSE risk.
result The path is as short as possible while maintaining optimal trade-off.
Bias-Variance Tradeoff in a Sliding Window Implementation of the Stochastic Gradient Algorithmstat.ML
The paper analyzes SW-SGD for MSE in biased and variance-reduced gradient estimators.
problem Analyzing MSE of SW-SGD in biased and variance-reduced gradient estimators.
method Using asymptotic normality, the paper characterizes SW-SGD's mean and variance, proving convergence and showing SW-SGD's superiority over SGD.
result SW-SGD incurs lower MSE than SGD on quadratic and convex problems.