← Optimization & Theory
Machine Learning › Optimization & Theory ›

Regularization

48 directly classified papers

Papers per year

Papers

Transfer Learning Meets Functional Linear Regression: No Negative Transfer Under Posterior Drift AAAI 2025
Towards Adversarially Robust Dataset Distillation by Curvature Regularization AAAI 2025
Improving Deep Learning Speed and Performance Through Synaptic Neural Balance AAAI 2025
Task-Driven Wavelets using Constrained Empirical Risk Minimization CVPR 2024
Why Do We Need Weight Decay in Modern Deep Learning? NIPS 2024
Learning from Others: Similarity-based Regularization for Mitigating Dataset Bias. ACL 2024
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness NIPS 2024
Curvature-Balanced Feature Manifold Learning for Long-Tailed Classification CVPR 2023
Scalable Theory-Driven Regularization of Scene Graph Generation Models AAAI 2023
HW-TSC’s Participation in the WMT 2023 Automatic Post Editing Shared Task EMNLP 2023
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints NIPS 2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning NIPS 2022
STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing EMNLP 2022
Critical Regularizations for Neural Surface Reconstruction in the Wild CVPR 2022
Representation Costs of Linear Neural Networks: Analysis and Design NIPS 2021
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization CVPR 2021
Kernel-convoluted Deep Neural Networks with Data Augmentation AAAI 2021
Regularising Fisher Information Improves Cross-lingual Generalisation EMNLP 2021
Adaptive Knowledge Driven Regularization for Deep Neural Networks AAAI 2021
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization NIPS 2021
Implicit Regularization in Matrix Sensing via Mirror Descent NIPS 2021
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression NIPS 2020
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks ICML 2020
Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles EMNLP 2020
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks AAAI 2020