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..
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
Authors: Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present illustrative experiments confirming our analysis. |
| Researcher Affiliation | Academia | 1CSML, Istituto Italiano di Tecnologia, Genoa, Italy 2Department of Computer Science, University College, London, UK 3Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Universit a La Sapienza, Rome, Italy. |
| Pseudocode | Yes | Algorithm 1 NSID; Algorithm 2 NSID-Bilevel; Algorithm 3 Stochastic fixed point iterations |
| Open Source Code | Yes | We provide the code to reproduce our experiments at https://github.com/prolearner/ nonsmooth_implicit_diff |
| Open Datasets | Yes | For the data poisoning setup we use the MNIST dataset. |
| Dataset Splits | Yes | We split the MNIST original train set into 30K example for training and 30K examples for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | We implement NSID by relying on Py Torch automatic differentiation for the computation of Jacobian-vector products. For AID and ITD, we use the existing Py Torch implementations. (No version numbers provided for PyTorch). |
| Experiment Setup | Yes | The appropriate choice for the step-size guarantees that Φ is a contraction, in our case we set it equal to 2{p L µ 2λ2q... We set the regularization parameters λ p0.02, 0.1q... we set it [minibatch size] equal to 10% of the training set... we set ηi a1{pa2 iq for (N)SID dec and η a1{a2 for (N)SID const, where a1 b1β and a2 b2β... In particular we set a1 0.5, a2 2 for the synthetic Elastic net experiment and a1 2, a2 0.01 for Data poisoning. |