arXiv research

This work precisely characterizes and improves the tradeoff between robustness and accuracy in linear regression.

New framework for network regression models accounting for community structure.

We derive formulas for F measures' standard error and confidence intervals.

A new method for fair PCA ensures balanced error across groups.

Researchers compare different gradient methods for ridge regression, finding conjugate gradients have similar performance.

This work improves confidence intervals for Cox model test error using nested CV.

New method improves privacy in linear regression with optimal error bounds.

Efficient classifier error estimation without re-training.

More data can widen the gap between robust and standard models against adversarial attacks.

K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-i.i.d. data. This paper analyzes …

ECN framework improves training on noisy structured labels.

Researchers compute Bayes error for classification models using normalizing flows.

Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how the adversarial …

Score matching errors are not sufficient for measuring diffusion model quality.

PPI++ outperforms gold-standard labels only if pseudo-labels are highly correlated.

Kernel thinning compresses distributions more effectively than i.i.d. sampling or standard thinning.

New geometric analysis shows $L^2$ score error is flawed for diffusion models.

Paper improves ML estimation from incomplete data with robust M-estimator.

Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general back…

This paper addresses GE estimation in non-standard settings using various resampling methods.

Study derives error decay rates for kernel classification under source and capacity conditions.

Random forest training yields confidence intervals for generalization error.

Unified NICEk metrics improve solar forecasting accuracy.

New TTP framework fuses control arms while controlling Type-I error.

The study categorizes reward errors in reinforcement learning, finding some can be beneficial.

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential we…

We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smal…

In this paper we introduce a parameter dependent class of Krylov-based methods, namely CD, for the solution of symmetric linear systems. We give evidence that in our proposal we generate sequences of conjugate directions, extending some properties of the standard Conjugate Gradient (CG) method, in order to preserve the…

The paper develops generalization bounds for deep compound Gaussian neural networks.

A new learning rule consistently reduces error over data samples.

This paper tackles worst-class error rate in classification tasks.

Paper unifies bias and variance models for classification.

The paper examines how adversarial training and noise affect neural network performance.

TCE measures calibration error with a test-based approach.

This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.

Ex ante forecast outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. Reapplying measurements of uncertainty about the estimation errors of the estimation errors of an estimation leads to branching counterfactuals. Such recursions of epistemic uncertainty …

Proposes an alternative method for quantifying uncertainty in complex models.

A new estimator improves financial econometrics by providing reliable inference.

Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded b…

New estimator adapts to various error distributions.

Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example. Then the ensemble has an error bound consisting of the average error bound for the member classifiers, a term for selectivity that varies fro…

Proposes a new SPVM model for RVM with more flexible priors.

New method stabilizes FQE by reweighting Bellman targets.

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to …

Study proposes local effective dimension to measure model capacity and generalization error.

We use the GARCH model with a fat-tailed error distribution described by a rational function and apply it for the stock price data on the Tokyo Stock Exchange. To determine the model parameters we perform the Bayesian inference to the model. The Bayesian inference is implemented by the Metropolis-Hastings algorithm wit…

Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to raise awareness on e…

This paper presents a new optimisation approach to train Deep Neural Networks (DNNs) with discriminative sequence criteria. At each iteration, the method combines information from the Natural Gradient (NG) direction with local curvature information of the error surface that enables better paths on the parameter manifol…