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.
Optimal Black-Box Reductions Between Optimization Objectives
Authors: Zeyuan Allen-Zhu, Elad Hazan
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,001 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,567 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 | We perform experiments to confirm our theoretical speed-ups obtained for Adapt Smooth and Adapt Reg. We work on minimizing Lasso and SVM objectives for the following three well-known datasets that can be found on the Lib SVM website [10]: covtype, mnist, and rcv1. |
| Researcher Affiliation | Academia | Zeyuan Allen-Zhu EMAIL Institute for Advanced Study & Princeton University Elad Hazan EMAIL Princeton University |
| Pseudocode | Yes | Algorithm 1 The Adapt Reg Reduction Input: an objective F( ) in Case 2 (smooth and not necessarily strongly convex); x0 a starting vector, σ0 an initial regularization parameter, T the number of epochs; an algorithm A that solves Case 1 of problem (1.1). Output: bx T . |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | We work on minimizing Lasso and SVM objectives for the following three well-known datasets that can be found on the Lib SVM website [10]: covtype, mnist, and rcv1. [10] Rong-En Fan and Chih-Jen Lin. LIBSVM Data: Classification, Regression and Multi-label. Accessed: 2015-06. |
| Dataset Splits | No | The paper mentions deferring 'dataset and implementation details' to the full version but does not provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or citations to predefined splits) in the provided text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper refers to methods like APCG [20] and SVRG [14] and mentions using datasets from the Lib SVM website [10], but it does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper describes some practical implementation details such as termination criteria for the oracle in the inner loop (e.g., duality gap below 1/4 or Euclidean norm below 1/3 of the previous epoch), and ranges for regularization weights (e.g., {10k, 3 · 10k : k ∈ Z}). However, it does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings for the experiments. |