structural risk minimization

When Can We Learn from Noisy Logical Data? Parameterized Complexity of Approximate Concept Fitting in Description Logics UAI 2026 EfficientLLaVA: Generalizable Auto-Pruning for Large Vision-language Models CVPR 2025 M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling AISTATS 2025 Regularization and Optimal Multiclass Learning COLT 2024 Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning CVPR 2024 Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning NeurIPS 2023 Spike Count Maximization for Neuromorphic Vision Recognition IJCAI 2023 Best of many worlds: Robust model selection for online supervised learning AISTATS 2019 Regularized Gradient Boosting NeurIPS 2019 Discriminative State Space Models NeurIPS 2017 Monotonic Calibrated Interpolated Look-Up Tables JMLR 2016 Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization JMLR 2015 Support vector machines with indefinite kernels ACML 2014 Empirical Risk Minimization with Approximations of Probabilistic Grammars NeurIPS 2010 Machine Learning with Data Dependent Hypothesis Classes JMLR 2002 Rademacher and Gaussian Complexities: Risk Bounds and Structural Results JMLR 2002