arXiv research

Corrects bias in random sampling matrices for improved ML methods.

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

New method detects and mitigates historical bias in data.

In classification problems, sampling bias between training data and testing data is critical to the ranking performance of classification scores. Such bias can be both unintentionally introduced by data collection and intentionally introduced by the algorithm, such as under-sampling or weighting techniques applied to i…

Adversarial domain adaptation reduces sample bias in high energy physics classifier.

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and …

We correct for sampling bias in training models to improve real-world performance.

Bayesian inference corrects bias in supervised learning datasets.

Bayesian method corrects bias in imbalanced datasets.

Paper connects sampling and labeling biases in large-output spaces.

The bias of the sample means of the arms in multi-armed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature. Existing results relate in precise ways the sign and magnitude of the bias to various sources of data adaptivity, but do not apply to the c…

The sample mean is among the most well studied estimators in statistics, having many desirable properties such as unbiasedness and consistency. However, when analyzing data collected using a multi-armed bandit (MAB) experiment, the sample mean is biased and much remains to be understood about its properties. For exampl…

The paper uses a graph autoencoder to learn unbiased plant-pollinator interaction embeddings.

Resampling outperforms reweighting for correcting biased data in machine learning models.

Unified framework suppresses model bias in semi-supervised learning with decoupled sampling control.

The paper addresses sampling bias in risk-based active learning.

For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversarial networks (GAN) had been proposed, we see the hope of small samples data augmentation (DA) with realistic fake data, and many works valid…

The paper tackles sampling bias in credit scoring models and proposes methods to improve their training and evaluation.

New active learning method uses combinatorial coverage to improve data transfer and reduce bias.

A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can b…

This paper balances bias and variance in adaptive importance sampling using mirror descent.

Deep learning models show bias and variance are aligned, not in trade-off.

Bias correction improves language model training performance.

New estimator corrects bias in CKA for sparsely sampled neurons.

This work overcomes bias in concave multi-objective reinforcement learning.

Study improves statistical power for detecting algorithmic bias in educational data.

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 …

Experience replay is an important technique for addressing sample-inefficiency in deep reinforcement learning (RL), but faces difficulty in learning from binary and sparse rewards due to disproportionately few successful experiences in the replay buffer. Hindsight experience replay (HER) was recently proposed to tackle…

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

New theory shows how learning algorithms can create a bias towards negative outcomes.

The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical lab…

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 …

New MCMC method corrects bias without extra cost.

Mitigates spurious correlations without bias labels.

Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-optimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a funct…

We consider the least-squares regression problem and provide a detailed asymptotic analysis of the performance of averaged constant-step-size stochastic gradient descent (a.k.a. least-mean-squares). In the strongly-convex case, we provide an asymptotic expansion up to explicit exponentially decaying terms. Our analysis…

The bias potential model explains how generative models can generalize or memorize samples.

Unified framework for mean testing under truncation bias.

Deep learning models can have low bias and variance, contrary to classical theory.

It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is typically not an unbiased estimator of its true mean. In this paper, we decouple three different sources of this selection bias: adaptive \emph{sampling} of arms, adaptive \emph{stopping} of the experiment, and adaptively \emph{…

Two new approaches improve decision-making in asset monitoring systems.

Improves transferability of representations from source to target domains with weights and invariant representations.

In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning. However, policy update from clipped statistics induces large bias in tasks with high action dimensions, and bias from clipping makes it di…

New characterization limits sampling with inexact scores.

Unified theory and debiasing framework for random oblique projections in high dimensions.

New method debiases selection bias in PU classification with exposure data.

Iterative method 'Concent' corrects spectrum bias in covariance matrices.

This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.