{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wasserstein-distributionally-robust-shallow","title":"Wasserstein Distributionally Robust Shallow Convex Neural Networks","arxiv_id":"2407.16800","date":"2024-07-23","proceeding":null,"authors":["Julien Pallage","Antoine Lesage-Landry"],"abstract":"In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear predictions when subject to adverse and corrupted datasets. Our approach is based on a new convex training program for $\\ReLU$-based shallow neural networks which allows us to cast the problem as an exact, tractable reformulation of its order-1 Wasserstein distributionally robust counterpart. Our training procedure is conservative, has low stochasticity, is solvable with open-source solvers, and is scalable to large industrial deployments. We provide out-of-sample performance guarantees, show that hard convex physical constraints can be enforced in the training program, and propose a mixed-integer convex post-training verification program to evaluate model stability. WaDiRo-SCNN aims to make neural networks safer for critical applications, such as in the energy sector. Finally, we numerically demonstrate the performance of our model on a synthetic experiment, a real-world power system application, i.e., the prediction of non-residential buildings' hourly energy consumption in the context of virtual power plants, and on benchmark datasets. The experimental results are convincing and showcase the strengths of the proposed model.","url_abs":"https://arxiv.org/abs/2407.16800v2","url_pdf":"https://arxiv.org/pdf/2407.16800v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"wasserstein-distributionally-robust-shallow","repo_url":"https://github.com/jupall/WaDiRo-SCNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}