arXiv:2606. 02740v1 Announce Type: cross Abstract: Gradient boosted decision trees require a stopping rule to avoid overfitting.
arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
arXiv:2607. 08522v1 Announce Type: new Abstract: The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation.
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
arXiv:2512. 18390v2 Announce Type: replace Abstract: Organizations often have an incumbent predictive model in production when new data sources become available.
Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead. We observe that probing partial traces at intermediate checkpoints can extract current answers without disrupting generation, revealing an evolving aggregate vote.
arXiv:2606. 12935v1 Announce Type: new Abstract: Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead.
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
arXiv:2606. 30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simple confidence or convergence thresholds.
arXiv:2607. 16802v1 Announce Type: new Abstract: Deep survival models are evaluated almost exclusively by the concordance index (C-index), yet they are commonly trained using likelihood objectives such as the Cox partial likelihood, discrete-time negative log-likelihood, and DeepHit likelihood.
arXiv:2601. 22669v3 Announce Type: replace Abstract: Federated Learning (FL) facilitates decentralized collaborative learning without transmitting raw data.
The paper argues that in offline preference optimization for reasoning models, applying gradients uniformly to all chosen–rejected pairs is inefficient and can be harmful. It introduces the concept of gradient utility, showing that a pair’s contribution depends on both informativeness and stability, and that high-gradient samples often lie in high‑curvature regions, causing noisy updates. To address this, the authors propose SAGE (Stability‑Aware Gradient Efficiency), which maintains difficulty‑stratified candidate pools and selects only high‑utility pairs for backpropagation, resulting in smoother optimization and better performance on mathematical reasoning benchmarks.
The paper introduces SHAP concentration as a pre‑deployment diagnostic for detecting when conformal prediction will fail under distribution shift, specifically in gradient‑boosted classifiers. Using a COVID‑19 supply‑chain case study, the authors show that higher feature‑importance concentration correlates with larger drops in coverage, while standard shift detectors cannot differentiate between catastrophic and robust outcomes. The diagnostic is validated on additional datasets, and a formal theorem links concentration to worsening conformity‑score bounds, though it does not capture global‑sensitivity failures in neural networks.