The paper describes a method to infer the signal-to-noise ratio in portfolio optimization.
problem Estimating the signal-to-noise ratio in portfolio optimization problems.
method A statistic similar to the Sharpe Ratio Information Criterion is used for inference.
result The method works well for reasonable sample and asset universe sizes.
Proposes isotonic recalibration for insurance pricing to ensure auto-calibration under low signal-to-noise ratio.
problem Ensuring auto-calibration in insurance pricing models to prevent cross-financing.
method Applies isotonic recalibration to regression models to achieve auto-calibration.
result Isotonically recalibrated regression functions have low complexity under low signal-to-noise ratio.
Missing data reduces signal-to-noise ratio, not sample size, for PCA.
problem Effect of missing data on PCA signal structure learning.
method Analytic and simulation studies of probabilistic PCA with missing data.
result Missing data effectively reduces signal-to-noise ratio, not sample size.
GRRT recovers sparse signals without prior sparsity or noise variance knowledge.
problem Recovering sparse signals without prior sparsity or noise variance knowledge.
method Generalized residual ratio thresholding (GRRT) for SOMP and BOMP.
result Finite sample and finite SNR guarantees for exact support recovery.
The signal-noise ratio of a portfolio of p assets, its expected return divided by its risk, is couched as an estimation problem on the sphere. When the portfolio is built using noisy data, the expected value of the signal-noise ratio is bounded from above via a Cramer-Rao bound, for the case of Gaussian returns. The bo…
Study detects signals in spiked Wigner models using log likelihood ratio.
problem Detecting signals in rank-one spiked Wigner models with non-Gaussian noise.
method Proved asymptotic normality of log likelihood ratio and computed error thresholds.
result Optimal signal-to-noise ratio threshold for reliable detection.
Improves diffusion models by controlling total variance and signal-to-noise-ratio.
problem Long sampling time in diffusion models.
method Total-Variance/Signal-to-Noise-Ratio (TV/SNR) disentangled framework.
result Improves generation performance by controlling TV and SNR independently.
The paper develops methods for conditional inference on the asset with the highest Sharpe ratio.
problem Performing inference on the asset with the highest Sharpe ratio among correlated assets.
method Conditional inference procedure using multivariate Sharpe ratio standard error, alternative tests, and asymptotic adjustments.
result The conditional inference procedure achieves nominal type I rate and maintains near-nominal rejection rates under the conditional null.
New techniques for compressive sensing without noise or signal statistics.
problem Support recovery in underdetermined linear regression models without prior noise and signal statistics.
method Proposes RRM and RRTA to operate OMP algorithm without noise variance or signal sparsity knowledge.
result Establishes high SNR consistency for OMP without prior noise and signal statistics.
Post hoc test for Sharpe ratio improves pairwise comparisons.
problem Improving pairwise comparisons of Sharpe ratios.
method Analogous to Tukey's test, applied after rejecting equal Signal-Noise ratios.
result Maintains nominal type I rate and is moderately powerful.
The paper develops a method to model high-dimensional data with many variables and weak signals.
problem Modeling high-dimensional dependent data with many explanatory variables and low signal-to-noise ratio.
method Penalized regression for high-dimensional data, factor modeling of residuals, high-dimensional white noise testing, projected Principal Component Analysis.
result Established asymptotic properties of the proposed method for high-dimensional data.
Study on Langevin dynamics for recovering planted signals in spiked matrix models.
problem Recovering a planted signal in spiked matrix models.
method Path-wise characterization of overlap using integro-differential equations and explicit formula derivation.
result Sharp phase transition in limiting overlap: positive in one regime, zero in another due to injected noise.
A test for weak signal detection in noisy data matrices.
problem Detecting a weak signal in a noisy Wigner matrix when the signal-to-noise ratio is small.
method Utilizes linear spectral statistics and hypothesis testing on the data matrix.
result The proposed test is optimal when the noise is Gaussian and can be improved with known noise density.
A new method for joint noise removal and trend estimation from sparse signals.
problem Jointly removing noise and estimating trends from sparse signals.
method PENDANTSS combines SOOT/SPOQ penalties with BEADS algorithm in a Trust-Region block alternating variable metric forward-backward approach.
result Outperforms comparable methods in deconvolving analytical chemistry signals.
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
problem Estimating precision matrices of asset returns in low signal-to-noise ratio environments.
method Non-linear factor model within deep learning framework, consistent estimator with error covariance estimator.
result Superior accuracy in simulations and empirical data.
Efficient algorithm for tensor PCA with improved time complexity.
problem Tensor PCA problem with signal-to-noise ratio constraint.
method Counting specific weighted hypergraphs to solve tensor PCA.
result Algorithm runs in time nC+o(1) for λn−4p signal-to-noise ratio. DP-GD improves CNN training accuracy with privacy, especially with low signal-to-noise ratios.
problem Privacy-preserving training of neural networks with crowdsourced data.
method Differentially private gradient descent (DP-GD) algorithm applied to two-layer CNNs.
result DP-GD can achieve superior generalization performance compared to GD, especially with low signal-to-noise ratios.
Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between mixed signals and clean signals. At this time, we use an objective function that numerically expresses the quality of a mapping by DNN. In …
A deep learning model improves seismic noise reduction.
problem Seismic noise attenuation in pre-stack data processing.
method N2N-Seismic model based on residual neural networks.
result The model significantly outperforms conventional approaches in noise reduction metrics.
DGPs with variational inference suffer from SNR issues that degrade gradient estimates, leading to unreliable training.
problem SNR issues in gradient estimates for DGPs with variational inference.
method Adapted doubly reparameterized gradient estimators for DGP training.
result Fix improves predictive performance of DGP models.
Optimal test for detecting signal in noisy matrix model.
problem Signal detection in noisy matrix models with unknown rank.
method Hypothesis test based on linear spectral statistics, optimal under Gaussian noise.
result Optimal test under Gaussian noise, improved with non-Gaussian noise.
Attention models can overfit without harming test performance.
problem Understanding benign overfitting in single-head attention models.
method Analyzing conditions for benign overfitting in a single-head softmax attention model.
result A single-head attention model can overfit without harming test performance under certain conditions.
AGNES accelerates gradient descent with noisy gradients.
problem Minimizing smooth convex and strongly convex functions with noisy gradients.
method Generalization of Nesterov's accelerated gradient descent algorithm for noisy conditions.
result AGNES achieves acceleration for noisy gradients with a constant of proportionality up to 1.
The paper studies phase transitions in random matrices and tensor unfolding for detecting signals.
problem Phase transitions in singular values and vectors of large random matrices.
method Analysis of singular values and vectors of long rectangular random matrices, and tensor unfolding algorithm for asymmetric rank-one spiked tensor models.
result An exact threshold for tensor unfolding to detect signals, independent of unfolding procedure.
A DL autoencoder tackles interference channels, improving SNR and INR.
problem Improving performance in interference channels with varying interference levels.
method Designing a DL neural network autoencoder for a k-user Gaussian interference channel, classifying interferences as weak to very strong.
result DL autoencoder significantly mitigates interference effects, especially with known α and low offset.
Alignment of neural network representations is influenced by SNR and sample size.
problem Understanding how neural network representations align across different conditions.
method Controlled training of neural networks on perturbed datasets, analyzing alignment and generalization.
result Alignment varies monotonically with SNR but non-monotonically with sample size, with minimal alignment near the interpolation threshold.
GD with large init shows incremental learning in matrix factorization.
problem Understanding GD's behavior with large initial values in matrix factorization.
method Signal-to-noise ratio concepts and inductive arguments.
result Uncovering an incremental learning phenomenon in GD with large initialization.
Study analyzes Hotelling-type tensor deflation for spiked tensors, providing insights into signal and noise.
problem Characterizing singular values and alignments in Hotelling-type tensor deflation.
method Asymptotic study of Hotelling-type tensor deflation in large dimensional regime using random tensor theory.
result Characterization of singular values and alignments at each step of the deflation procedure.
Proposes NCMN to remove feature correlation in multiplicative noise.
problem High feature correlation in multiplicative noise reduces network performance.
method Exploits batch normalization to remove correlation effect.
result Significantly improves performance on image classification tasks.
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
problem Optimizing medical imaging systems by assessing their performance using the Ideal Observer model.
method Uses supervised learning methods, specifically convolutional neural networks, to approximate the Ideal Observer for joint signal detection and localization tasks.
result Supervised learning-based methods can approximate the Ideal Observer for joint signal detection and localization tasks, as shown by comparisons to MCMC and analytical methods.
Enhances financial data signal-to-noise ratio using auto-encoders and mutual regularization.
problem Improving signal-to-noise ratio in financial data.
method Combining target and context variables, using auto-encoders with mutual regularization to learn common ground.
result Discover new regularities in financial time-series data.
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal. Therefore, (1) explaining away the structured noise in multiple-output regression is of paramount importanc…
Efficient method for tensor linear form inference with noisy incomplete data.
problem Statistical inference of tensor linear forms with incomplete and noisy observations.
method Initial estimate + debiasing + one-step power iteration.
result Optimal uncertainty quantification and statistical-to-computational gaps examined.
Improved diffusion models achieve state-of-the-art likelihoods in image density estimation.
problem Improving likelihood-based performance of diffusion models.
method Joint optimization of noise schedule and model parameters, using signal-to-noise ratio simplification.
result State-of-the-art likelihoods on image density estimation benchmarks, faster optimization.
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a top-down sensing procedure can identify these partitions, and hence the activa…
Spectrum selectively trains LLMs based on SNR to save resources.
problem Efficiently training large language models with limited resources.
method Targeting layer modules based on SNR for selective training.
result Spectrum achieves similar performance to full fine-tuning but with reduced VRAM usage.
Algorithm detects and estimates correlated signals in spiked matrices.
problem Detect and estimate correlated signals in spiked matrices.
method Proposes an efficient algorithm based on counting edge-decorated cycles.
result Algorithm succeeds under certain signal-to-noise ratio conditions.
This paper explores BDL hyperparameters for robust polynomial mapping with noise.
problem Designing BDL hyperparameters for robust function mapping with uncertainty quantification.
method Mapping Bayesian connectionist representations to polynomials of varying orders and noise types.
result Optimal network depth and ensemble size for prediction and uncertainty quantification.
MPNNs struggle with class-bottlenecks and heterophily, leading to performance limitations.
problem Performance limitations of MPNNs under heterophily and structural bottlenecks.
method A statistical framework decomposing model performance into SNR components and proving bounds on sensitivity.
result Optimal graph structures for maximizing higher-order homophily are disjoint unions of single-class and two-class-bipartite clusters.
Bayesian priors improve neural network performance on weak signals.
problem Challenges in encoding domain knowledge for weak signals in neural networks.
method Proposed a new joint prior over local scale parameters for feature sparsity and signal-to-noise ratio, optimized with Stein gradient.
result Improved prediction accuracy on various datasets, including genetics applications with weak and sparse signals.
FCN improves speech clarity in noisy environments.
problem Improving speech clarity in noisy environments.
method Fully convolutional neural network (FCN) for speech enhancement.
result FCN can generalize to new speakers and robust to varying noise.
Two-layer CNNs can overfit without harm under certain conditions.
problem Understanding when and how overfitting occurs in neural networks.
method Theoretical analysis of a two-layer CNN trained by gradient descent.
result A sharp phase transition between benign and harmful overfitting based on signal-to-noise ratio.
This paper compares traditional and new CSP methods for EEG classification in BCIs.
problem Improving signal-to-noise ratio in EEG signals for better BCI performance.
method Spatial filtering using traditional and new CSP methods with regularization.
result The traditional CSP method generally gives better results in binary classification.
New method separates and deconvolves signals from single-channel mixtures.
problem Separating and deconvolving individual sources from a single-channel mixture.
method Synthesizing-decomposition (S-D) approach using GAN for sources and optimization for filters and sources.
result Achieves PSNR improvements over existing methods in various tasks.
Study analyzes accuracy of tensor deflation in noisy conditions.
problem Analyzing accuracy of tensor deflation in noisy conditions.
method Asymptotic study of Hotelling-type tensor deflation in large tensor dimensions.
result Characterization of estimated singular values and singular vector alignments.
EigenBayes: A fast, adaptive Bayesian shrinkage approach for high-dimensional matrix factorization
problem Choosing the latent dimension k in factor models method Adaptive spectral shrinkage and empirical Bayes calibration
result Adapts to signal-to-noise ratio and shrinks superfluous components
Improved speech separation and enhancement using neural beamforming.
problem Challenging speech separation and enhancement in reverberant environments.
method Sequential neural beamforming combining spectral and spatial separation methods.
result Average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2% absolute reduction in speech recognition metric.
AMP algorithm for matrix tensor product model provides recovery conditions.
problem Generalization of standard spiked matrix models with multiple pairwise observations.
method Approximate message passing with optimal weighing and combining of estimates.
result Asymptotically exact performance description and necessary/sufficient recovery conditions.