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..
Provably Correct Automatic Sub-Differentiation for Qualified Programs
Authors: Sham M. Kakade, Jason D. Lee
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result shows that, under certain restrictions on our library of nonsmooth functions (standard in nonlinear programming), provably correct generalized subderivatives can be computed at a computational cost that is within a (dimension-free) factor of 6 of the cost of computing the scalar function itself. |
| Researcher Affiliation | Academia | Sham M. Kakade University of Washington EMAIL Jason D. Lee University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1: Straight Line Program for fpxq; Algorithm 2: The Reverse Mode of AD; Algorithm 3: Program for a Nonsmooth function gpxq; Algorithm 4: Re LU pxq; Algorithm 5: Re LU pxq; Algorithm 6: Automatic Subdifferentiation; Algorithm 7: Overloading the function gpxq; Algorithm 8: σpxq |
| Open Source Code | No | The paper does not provide a link to or explicitly state the release of its own source code. It mentions third-party libraries like TensorFlow and PyTorch. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and discusses computational costs in terms of abstract 'unit runtime cost' rather than specific hardware used for experiments. |
| Software Dependencies | No | The paper mentions existing software (TensorFlow, PyTorch) as background examples but does not specify software dependencies with version numbers for its own contributions or implementation. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |