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.
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Authors: Jonathan Lacotte, Yifei Wang, Mert Pilanci
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,040 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,784 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 | Finally, we show in Section 5 the empirical benefits of our adaptive method, compared to several standard optimization baselines. and In this section, we compare adaptive Newton Sketch (NSada) with other optimization methods in regularized logistic regression problems as in Example 4. The datasets used in the numerical experiments are collected from LIBSVM (Chih-Chung & Chih-Jen, 2011). For each dataset, we randomly split it into a training set and a test set with the ratio 1 : 1. |
| Researcher Affiliation | Academia | Jonathan Lacotte 1 Yifei Wang 1 Mert Pilanci 1 1Department of Electrical Engineering, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Effective dimension Newton sketch and Algorithm 2 Adaptive effective dimension Newton sketch |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The datasets used in the numerical experiments are collected from LIBSVM (Chih-Chung & Chih-Jen, 2011). |
| Dataset Splits | Yes | For each dataset, we randomly split it into a training set and a test set with the ratio 1 : 1. |
| Hardware Specification | Yes | All numerical experiments are executed on a Dell Power Edge R840 workstation. Specifically, we use 4 cores with 192GB ram for all compared methods. |
| Software Dependencies | No | The paper mentions software like 'Katyusha' and 'SVRG' and references LIBSVM, but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For SVRG and Katyusha, we use a batch size of 20. |