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.
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
Authors: Yilong Wang, Haishan Ye, Guang Dai, Ivor Tsang
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 22,288 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,141 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, our experimental results substantiate the effectiveness and efficiency of our algorithm. This section is dedicated to the empirical validation of our algorithm s effectiveness and superiority. Our experiments will focus on the quadratic minimization problem, whose objective function adheres to the form delineated in Eq. (1), characterized by min x Rd F(x) = 1 2x T Mx b T x + φ(x) |
| Researcher Affiliation | Collaboration | Yilong Wang 1 2 Haishan Ye 1 3 Guang Dai 3 Ivor W. Tsang 4 5 1Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi an Jiaotong University, China. 2This work was completed during the internship at SGIT AI Lab, State Grid Corporation of China. 3SGIT AI Lab, State Grid Corporation of China. 4CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore 5College of Computing and Data Science, NTU, Singapore. |
| Pseudocode | Yes | Algorithm 1 GSGD:Gaussian Sketched Gradient Descent Initialize: x0, h0 N(0, Id), stepsize η > 0 for k = 0, 1, do Sample u N(0, Id) gk = hk + uu ( f(xk) hk) hk+1 = hk + uu d+2 ( f(xk) hk) xk+1 = Proxαφ(xk ηgk) end for |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using the 'Appliances Energy Prediction' dataset but does not provide a direct link, DOI, repository name, or formal citation for accessing it. |
| Dataset Splits | No | The paper does not specify exact train/validation/test split percentages or sample counts for the datasets used in the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper mentions 'properly choose the step sizes' and that they 'should be proportional to O(1/tr(M)) and O(1/(dλ(M)))' but does not provide specific numerical values for hyperparameters or other detailed training configurations. |