arXiv research

A bias classifier is introduced to resist adversarial attacks.

Paper analyzes ECE bias and provides bounds for its estimation.

Study suggests using information flow measures to target interventions in neural networks.

Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed targe…

Bias and flexibility trade off in learning algorithms.

New method quantifies inductive bias for machine learning tasks.

New estimator reduces bias and variance issues in mutual information estimation.

Improves CNN robustness by reducing texture bias.

Proposes a neural network model to improve predictions in biased datasets.

Bayesian method corrects bias in imbalanced datasets.

We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset …

Proposes methods to correct bias and missing data in regression models.

Framework reduces simplicity bias in NNs, improving OOD generalization and robustness.

Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain fu…

New method learns diverse solutions in reinforcement learning without gradient bias.

(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can inherit gender bias from the data they were trained on. We investigate how this bias affects downstream classification tasks, using the case study of occupation classification (De-Arteaga et al.,2019). We show that traditional techniques for debi…

Paper tackles treatment leakage in text-based causal inference, proposing methods to mitigate bias.

New method neutralizes gender bias in word embeddings without losing semantic information.

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…

GWIB improves counterfactual regression by balancing latent distributions and reducing selection bias.

New framework explains neural network bias in solving differential equations.

Deep learning model estimates mutual information with low bias and variance.

Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next dep…

The paper addresses bias in survival analysis due to informative censoring.

VAEs improve generalization by regulating mutual information, contrary to intuition.

FR-Train improves fair and robust AI training by detecting and reducing poisoned data.

This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.

The paper explores the trade-off between bias and variance in high-dimensional models.

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…

Bayesian model learns bias for related tasks.

Unified framework for mean testing under truncation bias.

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

Anonymization reduces economic signal extraction from financial texts.

New algorithm achieves optimal regret in average reward MDPs without prior bias information.

Fine-tunes LLMs to correct bias in predictions.

New method mitigates bias without sensitive data using causal graph and variational autoencoder.

A new method for learning network representations that avoids information bias and sparsity.

Modeling bias in evaluation processes using optimization.

RS-PINN uses randomized smoothing to speed up high-dimensional PDE simulations without sacrificing accuracy.

Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs betwe…

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

Improved method for encoding contingency tables reduces mutual information bias.

OMIC improves matrix completion with orthonormal side information and nuclear-norm regularization.

This work introduces a bias-variance decomposition for proper scores, improving uncertainty estimation in predictive models.

Machine learning can improve 2SLS first stage predictions, but nonlinear methods often introduce bias.

New method detects and mitigates historical bias in data.

PAC-MCTS addresses biased search in LLM-guided planning by dynamically pruning.

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