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Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learning methods face significant bottlenecks in long-term autoregressive prediction, which exhibit excessive smoothing and fail to accurately track complex fluid dynamics. Our extensive experimental and spectral analysis of prevailing methods provides an interpretable explanation for this shortcoming, identifying Spectral …

Data Bias in Human Mobility is a Universal Phenomenon but is Highly Location-specific
Katinka den Nijs, Elisa Omodei, Vedran Sekara
https://arxiv.org/abs/2508.00149 https://

Data Bias in Human Mobility is a Universal Phenomenon but is Highly Location-specific
Large-scale human mobility datasets play increasingly critical roles in many algorithmic systems, business processes and policy decisions. Unfortunately there has been little focus on understanding bias and other fundamental shortcomings of the datasets and how they impact downstream analyses and prediction tasks. In this work, we study `data production', quantifying not only whether individuals are represented in big digital datasets, but also how they are represented in terms of how much data…

Covariate-informed link prediction with extreme taxonomic bias
Jennifer N. Kampe, Camille DeSisto, David B. Dunson
https://arxiv.org/abs/2506.23370 https:/…

Covariate-informed link prediction with extreme taxonomic bias
Biotic interactions provide a valuable window into the inner workings of complex ecological communities and capture the loss of ecological function often precipitated by environmental change. However, the financial and logistical challenges associated with collecting interaction data result in networks that are recorded with geographical and taxonomic bias, particularly when studies are narrowly focused. We develop an approach to reduce bias in link prediction in the common scenario in which da…

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

Targeted tuning of random forests for quantile estimation and prediction intervals
Matthew Berkowitz, Rachel MacKay Altman, Thomas M. Loughin
https://arxiv.org/abs/2507.01430

Targeted tuning of random forests for quantile estimation and prediction intervals
We present a novel tuning procedure for random forests (RFs) that improves the accuracy of estimated quantiles and produces valid, relatively narrow prediction intervals. While RFs are typically used to estimate mean responses (conditional on covariates), they can also be used to estimate quantiles by estimating the full distribution of the response. However, standard approaches for building RFs often result in excessively biased quantile estimates. To reduce this bias, our proposed tuning proc…

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft, Laure Zanna, Colleen McHugh, Liwei Jia
https://arxiv.org/abs/2505.18328

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully-coupled simulations: Hybrid_CPL (with feedbacks) and Hybrid_IO (without feedbacks). Relative to SPEAR, Hybrid…

The Impact of Pseudo-Science in Financial Loans Risk Prediction
Bruno Scarone, Ricardo Baeza-Yates
https://arxiv.org/abs/2507.16182 https://arxiv.org/pdf/2…

The Impact of Pseudo-Science in Financial Loans Risk Prediction
We study the societal impact of pseudo-scientific assumptions for predicting the behavior of people in a straightforward application of machine learning to risk prediction in financial lending. This use case also exemplifies the impact of survival bias in loan return prediction. We analyze the models in terms of their accuracy and social cost, showing that the socially optimal model may not imply a significant accuracy loss for this downstream task. Our results are verified for commonly used le…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
Richard Wyss, Ben B. Hansen, Georg Hahn, Lars van der Laan, Joshua K. Lin
https://arxiv.org/abs/2506.17760

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection operator (LASSO) is a data-adaptive prediction algorithm that has become the most widely used tool for large-scale PS estimation in these settings. However, recent work has shown that the use of data-adaptive algorithms for PS estimation can come at the cost of slow convergence rates, resulting in PS-based causal estimator…