arXiv research

New method corrects missing data bias in dimension reduction.

Ensembles improve classifier performance by reducing bias, not variance.

Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.

New method reduces version space for CNNs, improving active learning performance.

New method improves online covariance estimation for SGD.

New method reduces bias in estimating causal effects from discretized variables.

Paper debiases multiple word embedding biases simultaneously.

A new algorithm reduces bias and variance in distributionally robust optimization.

New technique reduces bias in CSO problems, improving sample complexity.

Random forests reduce bias and variance, especially in low SNR settings.

SkMM selects data for finetuning by balancing bias and variance.

This work studies the implicit bias of mini-batch SGD in classification.

We propose an approach to reduce the bias of ridge regression and regularization kernel network. When applied to a single data set the new algorithms have comparable learning performance with the original ones. When applied to incremental learning with block wise streaming data the new algorithms are more efficient due…

New methods reduce bias in estimating optimality gaps for risk-averse stochastic programs.

Automates bias control in reinforcement learning algorithms.

Study shows exponential error reduction in multiclass classification without bias-variance trade-off.

Recent years have seen an increased level of interest in pricing equity options under a stochastic volatility model such as the Heston model. Often, simulating a Heston model is difficult, as a standard finite difference scheme may lead to significant bias in the simulation result. Reducing the bias to an acceptable le…

VRER selectively reuses past observations to reduce variance in policy optimization.

The study addresses biases in evaluating molecular optimization methods and proposes methods to reduce these biases.

Paper tackles exposure bias in recommender systems using contrastive learning.

In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio. We show that the gap can be upper bounded by some form of dispersion measure of the likelihood ratio, which suggests the bias of variational inference can be reduced by making the distribution of the like…

Centered plug-in estimators reduce bias in Wasserstein distance estimation.

New method reduces bias and variance in OPE for large action spaces.

AI-generated variables bias regression estimates; methods correct for invalid inference.

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …

Reduces false positives in classifying rare online platforms.

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For binary classification and regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of …

This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the $L_2$ Boosting algorithm and provides a new statistical interpretation for $L_2$ Boosting. We analyze the behavior of the Boosting algorithm applied to commo…

UBM transfers bias mitigation from upstream to downstream tasks efficiently.

ADB framework improves OOD generalization by increasing ID bias during training.

EBQL reduces bias in Q-learning for improved performance.

Random forest performance depends on SNR and covariate characteristics.

We consider the off-policy evaluation problem in Markov decision processes with function approximation. We propose a generalization of the recently introduced \emph{emphatic temporal differences} (ETD) algorithm \citep{SuttonMW15}, which encompasses the original ETD($λ$), as well as several other off-policy evaluation …

A new Q-learning variant reduces underestimation bias in deep reinforcement learning.

BR-SNIS reduces bias in self-normalized IS without increasing variance.

Unified framework for output analysis using Monte Carlo sampling.

A new algorithm reduces bias in estimating model parameters.

Study on bias and extrapolation in LSA with Markovian data, showing bias reduction with Richardson-Romberg extrapolation.

Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018a) proposed an approach that significantly reduces the variance of infinite-horizon off-policy evaluation by estimating the stationar…

In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor's average loss over the training and test distributions also differ. In this work, we explore the potential of extreme dimension reduction, i.e. to very low dimensions, in improving the performance of imp…

New method improves regression estimates, reducing bias.

TMLE improves causal effect estimation in missing data scenarios with various positivity violations.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexper…

Improved estimator reduces bias in statistical learning models.

GRASP removes spurious correlations in fine-tuned models, improving task performance and reducing bias.

BEMA reduces bias in EMA, leading to faster convergence and better performance.

Study on nonsmooth contractive SA with constant stepsize and Q-learning.

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational speed. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness …