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Theory

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Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms NIPS 2021
Fast Axiomatic Attribution for Neural Networks NIPS 2021
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations NIPS 2021
Bounds all around: training energy-based models with bidirectional bounds NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights NIPS 2021
Hessian Eigenspectra of More Realistic Nonlinear Models NIPS 2021
Differentiable Spline Approximations NIPS 2021
Deep Learning on a Data Diet: Finding Important Examples Early in Training NIPS 2021
The balancing principle for parameter choice in distance-regularized domain adaptation NIPS 2021
When Are Solutions Connected in Deep Networks? NIPS 2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NIPS 2021
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model NIPS 2021
Stateful ODE-Nets using Basis Function Expansions NIPS 2021
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration NIPS 2021
Analytic Insights into Structure and Rank of Neural Network Hessian Maps NIPS 2021
On the Stochastic Stability of Deep Markov Models NIPS 2021
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels NIPS 2021
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks NIPS 2021
Towards Sharper Generalization Bounds for Structured Prediction NIPS 2021
Representation Costs of Linear Neural Networks: Analysis and Design NIPS 2021
The staircase property: How hierarchical structure can guide deep learning NIPS 2021
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks NIPS 2021
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification NIPS 2021
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds NIPS 2021
Unique Properties of Flat Minima in Deep Networks ICML 2020