The paper systematically evaluates regression objectives for tabular deep learning across many datasets and neural backbones, finding that most alternatives to Mean Squared Error (MSE) offer limited or inconsistent gains, while quantile loss consistently improves performance. It identifies a limitation of global early stopping: different noise levels in target regions reach optimal performance at different epochs, causing a compromise. To address this, the authors propose a heteroskedasticity‑aware extension of quantile loss using input‑dependent label smoothing, which outperforms standard quantile loss on several backbones.
By Danil Rudenko, Georgii Bulgakov, Nikolay Kartashev, Ivan Rubachev, Daniil Krasylnikov, Artem Babenko
arXiv:2608. 20326v1 Announce Type: cross Abstract: Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions.
By Parampreet Singh, Anushka Singh, Sumit Kumar, Vipul Arora
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can be trusted.
arXiv:2610.14996v1 Announce Type: new
Abstract: Choosing a few robust algorithms or schedules can require evaluating every candidate under many adverse scenarios, even though an agent only needs to i...
By Yuhe Sui, Yingzhi Tang
arXiv:2607. 22602v1 Announce Type: new Abstract: Inference-time scaling has emerged as a powerful paradigm for improving large language model reasoning, often delivering larger gains on difficult reasoning tasks than parameter scaling alone.
By Tingxin Yang, Zefeng Wang, Mengyue Wang, Xingcheng Zhou, Yunpu Ma
arXiv:2607. 05806v1 Announce Type: new Abstract: Training data for machine learning is routinely collected by a selection process the model never sees: loans are observed only when granted, outcomes only when a test was ordered.
By Gunner Levi Howe
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
By Rahul Vaze
arXiv:2606. 29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction.
By Craig Atkinson
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu
UQ-LOB is a lightweight, encoder‑agnostic module that adds uncertainty quantification to any pretrained limit order book (LOB) encoder. It offers two variants: UQ‑regression, which outputs a calibrated Gaussian over future tick displacement, and UQ‑classification, which outputs a categorical distribution over down/up/stationary. On 5.2 billion LOB events across seven cryptocurrency assets, UQ‑regression achieves near‑nominal 68 % interval coverage, and selecting the top 10 % most confident predictions boosts directional macro F1 by 0.11–0.15 for regression and 0.05–0.11 for classification, reaching F1 scores of 0.88 (down) and 0.83 (up) at a 5‑second horizon.
By Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys, Hao Dong, Juho Kanniainen
arXiv:2609.38806v1 Announce Type: cross
Abstract: Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on prob...
By Woosang Jeon, Jaeyeon Kim, Sham Kakade, Yilun Du, Amrit Singh Bedi, Arun Kumar Chithanar, Chul Lee, Taehyeong Kim, Sitan Chen
arXiv:2609.25388v1 Announce Type: cross
Abstract: A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal p...
By Xuelin Yang, Baihe Huang, Yilong Hou, Guido Imbens, Michael I. Jordan