Theory

On the Size and Approximation Error of Distilled Datasets NeurIPS 2023 On the spectral bias of two-layer linear networks NeurIPS 2023 On the Stability-Plasticity Dilemma of Class-Incremental Learning CVPR 2023 On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation ICLR 2023 On the Variance, Admissibility, and Stability of Empirical Risk Minimization NeurIPS 2023 On Total-Order HTN Plan Verification with Method Preconditions – An Extension of the CYK Parsing Algorithm AAAI 2023 On Translations between ML Models for XAI Purposes IJCAI 2023 On Undisputed Sets in Abstract Argumentation AAAI 2023 On using distribution-based compositionality assessment to evaluate compositional generalisation in machine translation EMNLP 2023 Operator learning with PCA-Net: upper and lower complexity bounds JMLR 2023 Optimal approximation using complex-valued neural networks NeurIPS 2023 Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression NeurIPS 2023 Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits ICML 2023 Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension COLT 2023 Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems AISTATS 2023 Optimal Sets and Solution Paths of ReLU Networks ICML 2023 Optimization or Architecture: How to Hack Kalman Filtering NeurIPS 2023 Optimizing Multiple Simultaneous Objectives for Voting and Facility Location AAAI 2023 Ordering-based Conditions for Global Convergence of Policy Gradient Methods NeurIPS 2023 Over-Training with Mixup May Hurt Generalization ICLR 2023 PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023 PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization NeurIPS 2023 PAC Generalization via Invariant Representations ICML 2023 PAC Prediction Sets for Large Language Models of Code ICML 2023 Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation IJCAI 2023