| Neural Networks can Learn Representations with Gradient Descent. | 0 | 0.34 | 2022 |
| Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games | 0 | 0.34 | 2022 |
| Optimization-Based Separations for Neural Networks. | 0 | 0.34 | 2022 |
| Offline Reinforcement Learning with Realizability and Single-policy Concentrability. | 0 | 0.34 | 2022 |
| Impact of Representation Learning in Linear Bandits | 0 | 0.34 | 2021 |
| A Theory Of Label Propagation For Subpopulation Shift | 0 | 0.34 | 2021 |
| Modeling from Features - a Mean-field Framework for Over-parameterized Deep Neural Networks. | 0 | 0.34 | 2021 |
| Near-Optimal Linear Regression Under Distribution Shift | 0 | 0.34 | 2021 |
| Shape Matters - Understanding the Implicit Bias of the Noise Covariance. | 0 | 0.34 | 2021 |
| On The Theory Of Policy Gradient Methods: Optimality, Approximation, And Distribution Shift | 0 | 0.34 | 2021 |
| Few-Shot Learning via Learning the Representation, Provably | 0 | 0.34 | 2021 |
| Kernel and Rich Regimes in Overparametrized Models | 0 | 0.34 | 2020 |
| Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation. | 0 | 0.34 | 2020 |
| How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. | 0 | 0.34 | 2020 |
| Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks | 0 | 0.34 | 2020 |
| Beyond Lazy Training for Over-parameterized Tensor Decomposition | 0 | 0.34 | 2020 |
| Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters | 0 | 0.34 | 2020 |
| Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot | 0 | 0.34 | 2020 |
| SGD Learns One-Layer Networks in WGANs | 0 | 0.34 | 2020 |
| Optimal transport mapping via input convex neural networks | 0 | 0.34 | 2020 |
| Generalized Leverage Score Sampling for Neural Networks | 0 | 0.34 | 2020 |
| Implicit Bias in Deep Linear Classification - Initialization Scale vs Training Accuracy. | 0 | 0.34 | 2020 |
| Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes | 0 | 0.34 | 2020 |
| Towards Understanding Hierarchical Learning: Benefits of Neural Representations | 0 | 0.34 | 2020 |
| Agnostic $Q$-learning with Function Approximation in Deterministic Systems - Near-Optimal Bounds on Approximation Error and Sample Complexity. | 0 | 0.34 | 2020 |
| Convergence of Adversarial Training in Overparametrized Neural Networks | 1 | 0.35 | 2019 |
| Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. | 3 | 0.38 | 2019 |
| Neural Temporal-Difference Learning Converges to Global Optima. | 4 | 0.39 | 2019 |
| Convergence of Adversarial Training in Overparametrized Networks. | 0 | 0.34 | 2019 |
| Multi-Turn Beam Search for Neural Dialogue Modeling. | 0 | 0.34 | 2019 |
| First-order methods almost always avoid strict saddle points | 7 | 0.55 | 2019 |
| Kernel and Deep Regimes in Overparametrized Models. | 0 | 0.34 | 2019 |
| Stochastic subgradient method converges on tame functions. | 10 | 0.59 | 2018 |
| Characterizing Implicit Bias in Terms of Optimization Geometry. | 9 | 0.49 | 2018 |
| Adding One Neuron Can Eliminate All Bad Local Minima. | 4 | 0.38 | 2018 |
| On the Margin Theory of Feedforward Neural Networks. | 9 | 0.49 | 2018 |
| Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition. | 2 | 0.36 | 2018 |
| Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks. | 1 | 0.35 | 2018 |
| On the Convergence and Robustness of Training GANs with Regularized Optimal Transport. | 4 | 0.41 | 2018 |
| Gradient Descent Finds Global Minima of Deep Neural Networks. | 30 | 0.69 | 2018 |
| Implicit Bias of Gradient Descent on Linear Convolutional Networks. | 13 | 0.51 | 2018 |
| Convergence of Gradient Descent on Separable Data. | 4 | 0.45 | 2018 |
| On the Power of Over-parametrization in Neural Networks with Quadratic Activation. | 20 | 0.63 | 2018 |
| Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization. | 2 | 0.37 | 2018 |
| Provably Correct Automatic Subdifferentiation for Qualified Programs. | 0 | 0.34 | 2018 |
| No Spurious Local Minima in a Two Hidden Unit ReLU Network | 1 | 0.35 | 2018 |
| Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced. | 10 | 0.65 | 2018 |
| Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement. | 11 | 0.50 | 2018 |
| Solving Approximate Wasserstein GANs to Stationarity. | 1 | 0.35 | 2018 |
| Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. | 1 | 0.38 | 2017 |
