Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Authors: Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence D’Alché-Buc
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Robustness benefits are emphasized by a theoretical stability analysis, as well as empirical improvements on structured data applications. In this section, we discuss some applications unlocked by vv-RKHSs with infinite dimensional outputs. In particular, structured prediction, structured representation learning, and functional regression are formally described, and numerical experiments highlight the benefits of the losses introduced. |
| Researcher Affiliation | Academia | 1LTCI, T el ecom Paris, Institut Polytechnique de Paris, France. |
| Pseudocode | Yes | Algorithm 1 Projected Gradient Descents (PGDs) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | YEAST dataset Finley and Joachims (2008); Metabolite dataset (Schymanski et al., 2017); drug dataset, introduced in Su et al. (2010); EMG dataset (Ramsay and Silverman, 2007). |
| Dataset Splits | Yes | Hyperparameters Λ, ϵ, κ have been selected among geometrical grids by cross-validation on the train dataset solely, and performances evaluated on the same test set as the above publications. Usefulness of minimizing the Huber loss is illustrated in Figure 8 by computing the Leave-One-Out (LOO) error associated to each model for various values of m. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as CPU or GPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions "Pystruct lib implementation (M uller and Behnke, 2014)" but does not provide a specific version number for this or any other software dependency relevant to reproducibility. |
| Experiment Setup | No | The paper states that "Hyperparameters Λ, ϵ, κ have been selected among geometrical grids by cross-validation on the train dataset solely" and that specific Λ values were "picked for their interesting behavior" with ϵ and κ "chosen to produce the best scores." However, it does not provide the concrete numerical values of these hyperparameters (e.g., learning rate, batch size, or the chosen Λ, ϵ, κ values for the reported results) within the main text. |