Cover Image

What Do Machine Learning Researchers Mean by "Reproducible"?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Raff, Edward, Benaroch, Michel, Samtani, Sagar, Farris, Andrew L.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack
by: Raff, Edward, et al.
Published: (2025)

What is Reproducibility in Artificial Intelligence and Machine Learning Research?
by: Desai, Abhyuday, et al.
Published: (2024)

SubstratumGraphEnv: Reinforcement Learning Environment (RLE) for Modeling System Attack Paths
by: Adewunmi, Bahirah, et al.
Published: (2026)

Differentially Private Iterative Screening Rules for Linear Regression
by: Khanna, Amol, et al.
Published: (2025)

Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
by: Mohammadi, Seyedali, et al.
Published: (2025)

Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations
by: Hoang, Huy, et al.
Published: (2025)

What Do Learned Models Measure?
by: Žliobaitė, Indrė
Published: (2026)

A Walsh Hadamard Derived Linear Vector Symbolic Architecture
by: Alam, Mohammad Mahmudul, et al.
Published: (2024)

What Do Latent Action Models Actually Learn?
by: Zhang, Chuheng, et al.
Published: (2025)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
by: Cooper, A. Feder, et al.
Published: (2024)

Reproducibility of Machine Learning-Based Fault Detection and Diagnosis for HVAC Systems in Buildings: An Empirical Study
by: Mukhtar, Adil, et al.
Published: (2025)

Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
by: Aguilar-Bejarano, Eduardo, et al.
Published: (2025)

Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
by: Khanna, Amol, et al.
Published: (2024)

Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
by: Alam, Mohammad Mahmudul, et al.
Published: (2024)

Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
by: Vranješ, Daniel, et al.
Published: (2024)

Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions
by: Shrestha, Sagar, et al.
Published: (2024)

LLM-Generated Explanations Do Not Suffice for Ultra-Strong Machine Learning
by: Ai, Lun, et al.
Published: (2025)

Epistemic Deep Learning: Enabling Machine Learning Models to Know When They Do Not Know
by: Manchingal, Shireen Kudukkil
Published: (2025)

Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
by: Ahmed, Waqas, et al.
Published: (2026)

Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization
by: Prieto, Nicolás Villagrán, et al.
Published: (2026)

Reproducibility study of FairAC
by: de Jong, Gijs, et al.
Published: (2024)

Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
by: Zeng, Zhiyuan, et al.
Published: (2024)

Learning Reconstructive Embeddings in Reproducing Kernel Hilbert Spaces via the Representer Theorem
by: Feito-Casares, Enrique, et al.
Published: (2026)

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
by: Stritzel, Oliver, et al.
Published: (2025)

Algebraic Quantum Intelligence: A New Framework for Reproducible Machine Creativity
by: Yano, Kazuo, et al.
Published: (2026)

A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning
by: Levada, Alexandre L. M.
Published: (2026)

Simple and Effective Transfer Learning for Neuro-Symbolic Integration
by: Daniele, Alessandro, et al.
Published: (2024)

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
by: Daniele, Alessandro, et al.
Published: (2022)

The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
by: Zimmer, Max, et al.
Published: (2026)

Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
by: Flageat, Manon, et al.
Published: (2023)

Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization
by: Srinivas, Sakhinana Sagar, et al.
Published: (2024)

On Variance Reduction in Learning Mean Flows
by: Lu, Juanwu, et al.
Published: (2026)

On the Replicability and Reproducibility of Deep Learning in Software Engineering
by: Liu, Chao, et al.
Published: (2020)

Improving Group Fairness in Knowledge Distillation via Laplace Approximation of Early Exits
by: Fasth, Edvin, et al.
Published: (2025)

Diversified Flow Matching with Translation Identifiability
by: Shrestha, Sagar, et al.
Published: (2025)

Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
by: Lade, Ankit Hemant, et al.
Published: (2026)

GPML: Graph Processing for Machine Learning
by: Jaber, Majed, et al.
Published: (2025)

Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
by: Srinivas, Sakhinana Sagar, et al.
Published: (2024)

Improve Machine Learning carbon footprint using Parquet dataset format and Mixed Precision training for regression models -- Part II
by: Antonopoulos, Andrew
Published: (2024)

Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning
by: Vaskov, Sean, et al.
Published: (2024)