Cover Image

Skewed Score: A statistical framework to assess autograders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dubois, Magda, Coppock, Harry, Giulianelli, Mario, Flesch, Timo, Luettgau, Lennart, Ududec, Cozmin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics
by: Luettgau, Lennart, et al.
Published: (2025)

Ask don't tell: Reducing sycophancy in large language models
by: Dubois, Magda, et al.
Published: (2026)

Seven simple steps for log analysis in AI systems
by: Dubois, Magda, et al.
Published: (2026)

Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language
by: Summerfield, Christopher, et al.
Published: (2025)

Hessian of Perplexity for Large Language Models by PyTorch autograd (Open Source)
by: Ilin, Ivan
Published: (2025)

A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
by: Re, Francesco Ignazio, et al.
Published: (2025)

Statistical Inference for Score Decompositions
by: Dimitriadis, Timo, et al.
Published: (2026)

MCMC-Correction of Score-Based Diffusion Models for Model Composition
by: Sjöberg, Anders, et al.
Published: (2023)

Skew-adaptive conformal prediction
by: F., Paulo C. Marques, et al.
Published: (2026)

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
by: Cheon, Harry, et al.
Published: (2024)

BOBA: Byzantine-Robust Federated Learning with Label Skewness
by: Bao, Wenxuan, et al.
Published: (2022)

A Generalized Unified Skew-Normal Process with Neural Bayes Inference
by: Wang, Kesen, et al.
Published: (2024)

Log analysis is necessary for credible evaluation of AI agents
by: Kirgis, Peter, et al.
Published: (2026)

A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data
by: Roy, Padmaksha
Published: (2023)

Skewness-Robust Causal Discovery in Location-Scale Noise Models
by: Klippert, Daniel, et al.
Published: (2025)

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning
by: Liu, Yuchen, et al.
Published: (2025)

Understanding the Role of Layer Normalization in Label-Skewed Federated Learning
by: Zhang, Guojun, et al.
Published: (2023)

Federated Unlearning Model Recovery in Data with Skewed Label Distributions
by: Yu, Xinrui, et al.
Published: (2024)

Skew-Probabilistic Neural Networks for Learning from Imbalanced Data
by: Naik, Shraddha M., et al.
Published: (2023)

Gini Score under Ties and Case Weights
by: Brauer, Alexej, et al.
Published: (2025)

Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
by: Pezoulas, Vasileios C., et al.
Published: (2025)

Domain-Skewed Federated Learning with Feature Decoupling and Calibration
by: Wang, Huan, et al.
Published: (2026)

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
by: Lin, Yingyu, et al.
Published: (2024)

A statistical physics framework for optimal learning
by: Mignacco, Francesca, et al.
Published: (2025)

SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training
by: Adnan, Mohammed, et al.
Published: (2026)

HRTF Estimation using a Score-based Prior
by: Thuillier, Etienne, et al.
Published: (2024)

Supervised Pattern Recognition Involving Skewed Feature Densities
by: Benatti, Alexandre, et al.
Published: (2024)

A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
by: Yan, Yunlu, et al.
Published: (2023)

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning
by: Robertshaw, Harry, et al.
Published: (2024)

LOCUS: A Distribution-Free Loss-Quantile Score for Risk-Aware Predictions
by: Barreto, Matheus, et al.
Published: (2026)

Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks
by: Freiesleben, Timo
Published: (2026)

Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
by: Ayday, Nil, et al.
Published: (2025)

The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
by: Du, Mengyao, et al.
Published: (2024)

Loss Functions in Diffusion Models: A Comparative Study
by: Kumar, Dibyanshu, et al.
Published: (2025)

Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
by: Zhao, Hanyang, et al.
Published: (2024)

A Study of Skews, Imbalances, and Pathological Conditions in LLM Inference Deployment on GPU Clusters detectable from DPU
by: Moye, Javed I. Khan an Henry Uwabor
Published: (2025)

Open-World Evaluations for Measuring Frontier AI Capabilities
by: Kapoor, Sayash, et al.
Published: (2026)

Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
by: Watanabe, Shotaro, et al.
Published: (2022)

Decomposition of Water Demand Patterns Using Skewed Gaussian Distributions for Behavioral Insights and Operational Planning
by: Elkayam, Roy
Published: (2025)

Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy
by: Robertshaw, Harry, et al.
Published: (2025)