Sathya N. Ravi, Abhay Venkatesh, Glenn M. Fung, Vikas Singh. Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. pages 5487-5494, AAAI Press, 2020. [doi]
@inproceedings{RaviVFS20, title = {Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains}, author = {Sathya N. Ravi and Abhay Venkatesh and Glenn M. Fung and Vikas Singh}, year = {2020}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/5999}, researchr = {https://researchr.org/publication/RaviVFS20}, cites = {0}, citedby = {0}, pages = {5487-5494}, booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020}, publisher = {AAAI Press}, isbn = {978-1-57735-823-7}, }