Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Authors: Zhan Shi, Xinhua Zhang, Yaoliang Yu
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the theoretical findings through extensive experiments on two example applications: adversarial prediction and LPboosting. |
| Researcher Affiliation | Academia | Zhan Shi Xinhua Zhang University of Illinois at Chicago Chicago, Illinois 60661 {zshi22,zhangx}@uic.edu Yaoliang Yu University of Waterloo Waterloo, ON, N2L3G1 yaoliang.yu@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1: Breg-SVRG for Saddle-Point |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code will be made available. |
| Open Datasets | Yes | We experimented on the adult dataset from the UCI repository, which we partitioned into n = 32, 561 training examples and 16,281 test examples, with m = 123 features. |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention a validation set or its split details. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We set λ = γ = 0.01 and ν = 0.1 due to its best prediction accuracy. We tried a range of values of the step size η, and the best we found was 10 3 for Entropy-SVRG and 10 6 for Euclidean-SVRG (larger step size for Euclidean-SVRG fluctuated even worse). For both methods, m = 32561/50 gave good results. We fixed µ = 1, λ = 0.01 for the ionosphere dataset, and µ = 1, λ = 0.1 for the synthetic dataset. |