Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HOUDINI: Escaping from Moderately Constrained Saddles
Authors: Dmitrii Avdiukhin, Grigory Yaroslavtsev
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide experimental results in the full version. |
| Researcher Affiliation | Academia | 1Indiana University, Bloomington, IN 2George Mason University, Fairfax, VA |
| Pseudocode | Yes | Algorithm 1: HOUDINIESCAPECORNER: Escaping from a corner for a quadratic function, Algorithm 2: FINDINSIDECORNER(x, A), Algorithm 3: HOUDINIESCAPE(x, S, δ): Escaping from a saddle point, Algorithm 4: FINDINSIDE(x, δ, (M , v ), (r , S )) |
| Open Source Code | No | The paper mentions providing "experimental results in the full version" but does not state that source code for the methodology is openly available or provide a link. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with specific datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details, hyperparameters, or training configurations. |