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Optimization

14,983 papers

Papers per year

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Papers

On the Generalization Benefit of Noise in Stochastic Gradient Descent ICML 2020
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation ICML 2020
Bridging the Gap Between f-GANs and Wasserstein GANs ICML 2020
Provably Efficient Model-based Policy Adaptation ICML 2020
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking ICML 2020
An EM Approach to Non-autoregressive Conditional Sequence Generation ICML 2020
Quantized Decentralized Stochastic Learning over Directed Graphs ICML 2020
Reinforcement Learning for Integer Programming: Learning to Cut ICML 2020
Taylor Expansion Policy Optimization ICML 2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization ICML 2020
StochasticRank: Global Optimization of Scale-Free Discrete Functions ICML 2020
On the Global Optimality of Model-Agnostic Meta-Learning ICML 2020
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach ICML 2020
Optimizing Data Usage via Differentiable Rewards ICML 2020
Bandits for BMO Functions ICML 2020
When deep denoising meets iterative phase retrieval ICML 2020
Loss Function Search for Face Recognition ICML 2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data ICML 2020
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems ICML 2020
The Implicit and Explicit Regularization Effects of Dropout ICML 2020
Learning to Rank Learning Curves ICML 2020
Is Local SGD Better than Minibatch SGD? ICML 2020
Obtaining Adjustable Regularization for Free via Iterate Averaging ICML 2020
DeltaGrad: Rapid retraining of machine learning models ICML 2020
On the Noisy Gradient Descent that Generalizes as SGD ICML 2020