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Theory

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Papers

PAC-Bayes-Chernoff bounds for unbounded losses NIPS 2024
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training NIPS 2024
Least Squares Regression Can Exhibit Under-Parameterized Double Descent NIPS 2024
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling NIPS 2024
Nonlinear dynamics of localization in neural receptive fields NIPS 2024
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks? NIPS 2024
Generalization of Hamiltonian algorithms NIPS 2024
Evaluating the World Model Implicit in a Generative Model NIPS 2024
Optimal deep learning of holomorphic operators between Banach spaces NIPS 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof NIPS 2024
On the Parameter Identifiability of Partially Observed Linear Causal Models NIPS 2024
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions NIPS 2024
High-dimensional (Group) Adversarial Training in Linear Regression NIPS 2024
Replicable Uniformity Testing NIPS 2024
Low Degree Hardness for Broadcasting on Trees NIPS 2024
Learning to compute Gröbner bases NIPS 2024
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs NIPS 2024
Bridging OOD Detection and Generalization: A Graph-Theoretic View NIPS 2024
Topological obstruction to the training of shallow ReLU neural networks NIPS 2024
Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame NIPS 2024
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory NIPS 2024
Separations in the Representational Capabilities of Transformers and Recurrent Architectures NIPS 2024
On the Ability of Developers' Training Data Preservation of Learnware NIPS 2024
Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure NIPS 2024
Polyhedral Complex Derivation from Piecewise Trilinear Networks NIPS 2024