Imagen de Portada

A hierarchical decomposition for explaining ML performance discrepancies

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Feng, Jean, Singh, Harvineet, Xia, Fan, Subbaswamy, Adarsh, Gossmann, Alexej
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
por: Feng, Jean, et al.
Publicado: (2023)

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift
por: Singh, Harvineet, et al.
Publicado: (2025)

LLMs Judging LLMs: A Simplex Perspective
por: Vossler, Patrick, et al.
Publicado: (2025)

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design
por: Singh, Adarsh
Publicado: (2025)

HistoART: Histopathology Artifact Detection and Reporting Tool
por: Kahaki, Seyed, et al.
Publicado: (2025)

Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
por: Lobo, Elita, et al.
Publicado: (2024)

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
por: Drenkow, Nathan, et al.
Publicado: (2025)

Enhancing Actuarial Non-Life Pricing Models via Transformers
por: Brauer, Alexej
Publicado: (2023)

A Novel Plug-and-Play Approach for Adversarially Robust Generalization
por: Maurya, Deepak, et al.
Publicado: (2022)

Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning
por: Li, Wenqing, et al.
Publicado: (2026)

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

BALI: Learning Neural Networks via Bayesian Layerwise Inference
por: Kurle, Richard, et al.
Publicado: (2024)

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
por: Köhler, David, et al.
Publicado: (2024)

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
por: Sun, Xudong, et al.
Publicado: (2024)

Holistic Artificial Intelligence in Medicine; improved performance and explainability
por: Petridis, Periklis, et al.
Publicado: (2025)

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
por: Rabeh, Ali, et al.
Publicado: (2024)

DomainLab: A modular Python package for domain generalization in deep learning
por: Sun, Xudong, et al.
Publicado: (2024)

Car Drag Coefficient Prediction from 3D Point Clouds Using a Slice-Based Surrogate Model
por: Singh, Utkarsh, et al.
Publicado: (2026)

Mamba Adaptive Anomaly Transformer with association discrepancy for time series
por: Sellam, Abdellah Zakaria, et al.
Publicado: (2025)

Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition
por: Quintero, Manuel, et al.
Publicado: (2026)

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
por: Lo, Yui, et al.
Publicado: (2024)

Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM
por: Kumarappan, Adarsh, et al.
Publicado: (2025)

Inverse problem regularization with hierarchical variational autoencoders
por: Prost, Jean, et al.
Publicado: (2023)

Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction
por: Kumar, Adarsh
Publicado: (2025)

Maximum mean discrepancies of Farey sequences
por: Karvonen, Toni, et al.
Publicado: (2024)

Density estimation via mixture discrepancy and moments
por: Lei, Zhengyang, et al.
Publicado: (2025)

A hierarchical loss and its problems when classifying non-hierarchically
por: Wu, Cinna, et al.
Publicado: (2017)

Latent Matters: Learning Deep State-Space Models
por: Klushyn, Alexej, et al.
Publicado: (2026)

Automating Deception: Scalable Multi-Turn LLM Jailbreaks
por: Kumarappan, Adarsh, et al.
Publicado: (2025)

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
por: Kumarappan, Adarsh, et al.
Publicado: (2026)

PRISM: A hierarchical multiscale approach for time series forecasting
por: Chen, Zihao, et al.
Publicado: (2025)

3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints
por: Rabeh, Ali, et al.
Publicado: (2025)

Statistics and explainability: a fruitful alliance
por: Ghidini, Valentina
Publicado: (2024)

Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
por: Esteghamati, Mohsen Zaker
Publicado: (2025)

How to explain grokking
por: Kozyrev, S. V.
Publicado: (2024)

A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval
por: Barik, Adarsh, et al.
Publicado: (2024)

A circuit for predicting hierarchical structure in-context in Large Language Models
por: Saanum, Tankred, et al.
Publicado: (2025)

Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
por: Averyanov, Yaroslav, et al.
Publicado: (2020)

Infinite hierarchical contrastive clustering for personal digital envirotyping
por: Huang, Ya-Yun, et al.
Publicado: (2025)

A Self-explaining Neural Architecture for Generalizable Concept Learning
por: Sinha, Sanchit, et al.
Publicado: (2024)