Globally Optimal Learning for Structured Elliptical Losses
Authors: Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the empirical appeal of using these losses for regression on synthetic and real-life data. |
| Researcher Affiliation | Collaboration | Yoav Wald Hebrew University yoav.wald@mail.huji.ac.il Nofar Noy Hebrew University nofar.noy@mail.huji.ac.il Ami Wiesel Google Research and Hebrew University awiesel@google.com Gal Elidan Google Research and Hebrew University elidan@google.com |
| Pseudocode | Yes | Algorithm 1 Minimization Majorization for Elliptical Markov Random Fields |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | Instances of {zi}m i=1 are drawn from multivariate Generalized Gaussian distributions [22] |
| Dataset Splits | Yes | We use data on the years between 2004 and mid-2011 (excluding the mid-2007 to mid-2009 financial crisis) as training data and test over the values from then until 2015. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'sklearn function make_sparse_psd [23]' but does not provide a specific version number for scikit-learn or any other software dependency. |
| Experiment Setup | No | The paper describes the setup for synthetic and real-life experiments, including data sources and tasks, but it does not provide specific details such as hyperparameters, learning rates, or batch sizes. |