Tootfinder

Prediction-Powered Inference with Inverse Probability Weighting
Jyotishka Datta, Nicholas G. Polson
https://arxiv.org/abs/2508.10149 https://arxiv.org/pdf/…

Prediction-Powered Inference with Inverse Probability Weighting
Prediction-powered inference (PPI) is a recent framework for valid statistical inference with partially labeled data, combining model-based predictions on a large unlabeled set with bias correction from a smaller labeled subset. We show that PPI can be extended to handle informative labeling by replacing its unweighted bias-correction term with an inverse probability weighted (IPW) version, using the classical Horvitz--Thompson or Hájek forms. This connection unites design-based survey samplin…

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Jiayu Yao, Shenghua Liu, Yiwei Wang, Rundong Cheng, Lingrui Mei, Baolong Bi, Zhen Xiong, Xueqi Cheng
https://arxiv.org/abs/2510.12185

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. We present the first systematic study of temporal bias in LALMs, revealing a key limitation in their timestamp prediction. For example, when asked "At which second does the lecturer introduce the key formula?", models often predict timestamps that are consistently earlier or later than the ground truth. Through control…

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
https://arxiv.org/abs/2508.07617

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would w…

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Dakai Zhai, Jiong Gao, Boya Du, Junwei Xu, Qijie Shen, Jialin Zhu, Yuning Jiang
https://arxiv.org/abs/2510.09393

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems.Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a systemic training bias toward high-activity users that overshadows the needs of low-activity users.To address these challenges, we propose ChoirR…

Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
https://arxiv.org/abs/2510.06945 https://

Fisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the \emph{effective dimension} and the \emph{bias} of a model towards a given task, investigating how these affe…

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Akshit Achara, Esther Puyol Anton, Alexander Hammers, Andrew P. King
https://arxiv.org/abs/2509.09558

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such …

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…

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Joshua R. Jandrell, Mitchell A. Cox
https://arxiv.org/abs/2508.19751 https://

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Modelling the effects of perturbations on optical fields often requires learning highly oscillatory complex-valued functions. Standard multi-layer perceptrons (MLPs) struggle with this task due to an inherent spectral bias, preventing them from fitting high-frequency sinusoids. To overcome this, we incorporate Fourier features - a set of predefined sinusoids dependent on the perturbation - as an additional network input. This reframes the learning problem from approximating a complex function t…

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)…

Optimizing Token Choice for Code Watermarking: A RL Approach
Zhimeng Guo, Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Minhao Cheng
https://arxiv.org/abs/2508.11925 https…

Optimizing Token Choice for Code Watermarking: A RL Approach
The need for detecting LLM-generated code necessitates watermarking systems capable of operating within its highly structured and syntactically constrained environment. To address this, we introduce CodeTracer, an innovative adaptive code watermarking framework underpinned by a novel reinforcement learning training paradigm. At its core, CodeTracer features a policy-driven approach that utilizes a parameterized model to intelligently bias token choices during next-token prediction. This strateg…

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
Qihang Zhao, Xiaoyang Zheng, Ben Chen, Zhongbo Sun, Chenyi Lei
https://arxiv.org/abs/2509.19876

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise. Current methods predominantly follow an "identify-aggregate" paradigm, assuming sequences immutably reflect user preferences while overlooking the organic entanglement of noise and genuine interest. Moreover, they output static, context-agnostic representations, failing to adapt to dynamic intent shifts under varying Query-User-Item-C…