Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
One-step differentiation of iterative algorithms
Authors: Jerome Bolte, Edouard Pauwels, Samuel Vaiter
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,865 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,649 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Several numerical examples illustrate the well-foundedness of the one-step estimator. 4 Numerical experiments. We illustrate our findings on three different problems. |
| Researcher Affiliation | Academia | Jérôme Bolte Toulouse School of Economics, Université Toulouse Capitole, Toulouse, France. Edouard Pauwels Toulouse School of Economics (IUF), Toulouse, France. Samuel Vaiter CNRS & Université Côte d Azur, Laboratoire J. A. Dieudonné. Nice, France. |
| Pseudocode | Yes | Algorithm 1: Automatic, Algorithm 2: Implicit, Algorithm 3: One-step (in Table 1), and Figure 1: Implementation of Algorithms 1, 2 and 3 in jax. |
| Open Source Code | No | The paper provides code snippets in Figure 1 to illustrate implementation in 'jax', and mentions 'jax' as a framework, but it does not state that the specific code for the methodology described in this paper is open-source or publicly available, nor does it provide a link to such a repository. |
| Open Datasets | Yes | Weighted ridge using gradient descent. We consider a weighted ridge problem... on the data set cpusmall provided by Lib SVM [17] |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, and test dataset splits for its experiments. For instance, in 'Logistic regression using Newton's algorithm' or 'Interior point solver for quadratic programming', no split information is given. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'jax', 'pytorch', 'Lib SVM [17]', and 'cvxopt [43]', but it does not specify version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | Yes | Logistic regression using Newton's algorithm... λ > 0 is a regularization parameter... Newton's method which we implement in jax using backtracking line search (Wolfe condition). and Weighted ridge using gradient descent... F(x, θ) = x − α∇fθ(x) with x0(θ) = 0, and we consider the K-step truncated Jacobian propagation F = F K θ with K = 1/κ where κ is the effective condition number of the Hessian... for two types of step-sizes. Left column: small learning rate 1/L. Right column: big learning rate 2/(µ+L). |