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Theory

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Papers

Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time ICML 2020
Reliable Fidelity and Diversity Metrics for Generative Models ICML 2020
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors ICML 2020
LEEP: A New Measure to Evaluate Transferability of Learned Representations ICML 2020
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation ICML 2020
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis ICML 2020
Closing the convergence gap of SGD without replacement ICML 2020
Universal Equivariant Multilayer Perceptrons ICML 2020
Finite-Time Convergence in Continuous-Time Optimization ICML 2020
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing ICML 2020
Universal Average-Case Optimality of Polyak Momentum ICML 2020
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures ICML 2020
Neural Kernels Without Tangents ICML 2020
Channel Equilibrium Networks for Learning Deep Representation ICML 2020
On Conditional Versus Marginal Bias in Multi-Armed Bandits ICML 2020
On the Generalization Benefit of Noise in Stochastic Gradient Descent ICML 2020
The Many Shapley Values for Model Explanation ICML 2020
No-Regret Exploration in Goal-Oriented Reinforcement Learning ICML 2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations ICML 2020
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network ICML 2020
On the Global Optimality of Model-Agnostic Meta-Learning ICML 2020
Continuous Graph Neural Networks ICML 2020
Maximum-and-Concatenation Networks ICML 2020
On the Number of Linear Regions of Convolutional Neural Networks ICML 2020
Randomized Smoothing of All Shapes and Sizes ICML 2020