Learning Theory

On Margin Maximization in Linear and ReLU Networks NeurIPS 2022 On Margins and Derandomisation in PAC-Bayes AISTATS 2022 On Margins and Generalisation for Voting Classifiers NeurIPS 2022 On Measuring Excess Capacity in Neural Networks NeurIPS 2022 On Non-local Convergence Analysis of Deep Linear Networks ICML 2022 On Optimal Learning Under Targeted Data Poisoning NeurIPS 2022 On PAC-Bayesian reconstruction guarantees for VAEs AISTATS 2022 On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data ACML 2022 On reporting scores and agreement for error annotation tasks EMNLP 2022 On Robust Multiclass Learnability NeurIPS 2022 On the Complexity of Adversarial Decision Making NeurIPS 2022 On the Complexity of Inductively Learning Guarded Clauses AAAI 2022 On the Convergence of Certified Robust Training with Interval Bound Propagation ICLR 2022 On the Convergence of Fictitious Play: A Decomposition Approach IJCAI 2022 On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning ICLR 2022 On the Double Descent of Random Features Models Trained with SGD NeurIPS 2022 On the Effectiveness of Iterative Learning Control L4DC 2022 On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias NeurIPS 2022 On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model NAACL 2022 On the Effects of Artificial Data Modification ICML 2022 On the Epistemic Limits of Personalized Prediction NeurIPS 2022 On the Frequency-bias of Coordinate-MLPs NeurIPS 2022 On the Generalization Analysis of Adversarial Learning ICML 2022 On the generalization of learning algorithms that do not converge NeurIPS 2022 On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications ICLR 2022