Provably Correct Automatic Sub-Differentiation for Qualified Programs
Authors: Sham M. Kakade, Jason D. Lee
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 sham@cs.washington.edu Jason D. Lee University of Southern California jasonlee@marshall.usc.edu |
| 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. |