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Theory

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Papers

A Theoretical Analysis of the Learning Dynamics under Class Imbalance ICML 2023
Towards Reliable Neural Specifications ICML 2023
Hidden Symmetries of ReLU Networks ICML 2023
Width and Depth Limits Commute in Residual Networks ICML 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023
Tighter Analysis for ProxSkip ICML 2023
Understanding the Impact of Adversarial Robustness on Accuracy Disparity ICML 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models ICML 2023
Unveiling the Latent Space Geometry of Push-Forward Generative Models ICML 2023
Maximal Initial Learning Rates in Deep ReLU Networks ICML 2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing ICML 2023
On the Expressive Power of Geometric Graph Neural Networks ICML 2023
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization ICML 2023
Equivariance with Learned Canonicalization Functions ICML 2023
Data Representations’ Study of Latent Image Manifolds ICML 2023
Rethinking Backdoor Attacks ICML 2023
PAC Prediction Sets for Large Language Models of Code ICML 2023
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach ICML 2023
Regularizing Towards Soft Equivariance Under Mixed Symmetries ICML 2023
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network ICML 2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice ICML 2023
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks ICML 2023
Graph Neural Tangent Kernel: Convergence on Large Graphs ICML 2023
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation ICML 2023