Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |