Learning Theory

Understanding Expressivity of GNN in Rule Learning ICLR 2024 Understanding Forgetting in Continual Learning with Linear Regression ICML 2024 Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024 Understanding Heterophily for Graph Neural Networks ICML 2024 Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective NeurIPS 2024 Understanding MLP-Mixer as a wide and sparse MLP ICML 2024 Understanding Model Selection for Learning in Strategic Environments NeurIPS 2024 Understanding Progressive Training Through the Framework of Randomized Coordinate Descent AISTATS 2024 Understanding prompt engineering may not require rethinking generalization ICLR 2024 Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data NeurIPS 2024 Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation ICML 2024 Understanding Surprising Generalization Phenomena in Deep Learning AAAI 2024 Understanding the Effects of RLHF on LLM Generalisation and Diversity ICLR 2024 Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling NeurIPS 2024 Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective NeurIPS 2024 Understanding the Generalization Benefits of Late Learning Rate Decay AISTATS 2024 Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error ICML 2024 Understanding the Learning Dynamics of Alignment with Human Feedback ICML 2024 Understanding the Role of Equivariance in Self-supervised Learning NeurIPS 2024 Understanding Transformer Reasoning Capabilities via Graph Algorithms NeurIPS 2024 Understanding Unimodal Bias in Multimodal Deep Linear Networks ICML 2024 Understanding Visual Feature Reliance through the Lens of Complexity NeurIPS 2024 Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling UAI 2024 Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets JMLR 2024 Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning NeurIPS 2024