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Optimization

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Papers

Preferential Bayesian Optimization ICML 2017
Automated Curriculum Learning for Neural Networks ICML 2017
Data-Efficient Policy Evaluation Through Behavior Policy Search ICML 2017
Joint Dimensionality Reduction and Metric Learning: A Geometric Take ICML 2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space ICML 2017
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks ICML 2017
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control ICML 2017
Bayesian Optimization with Tree-structured Dependencies ICML 2017
StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent ICML 2017
Multi-fidelity Bayesian Optimisation with Continuous Approximations ICML 2017
Cost-Optimal Learning of Causal Graphs ICML 2017
Sub-sampled Cubic Regularization for Non-convex Optimization ICML 2017
Co-clustering through Optimal Transport ICML 2017
Conditional Accelerated Lazy Stochastic Gradient Descent ICML 2017
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization ICML 2017
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations ICML 2017
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms ICML 2017
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization ICML 2017
Exact MAP Inference by Avoiding Fractional Vertices ICML 2017
Learning Gradient Descent: Better Generalization and Longer Horizons ICML 2017
Frame-based Data Factorizations ICML 2017
Global optimization of Lipschitz functions ICML 2017
Variational Boosting: Iteratively Refining Posterior Approximations ICML 2017
Device Placement Optimization with Reinforcement Learning ICML 2017
Tight Bounds for Approximate Carathéodory and Beyond ICML 2017