Optimization
17,731 papers
Papers per year
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768
1,185
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Papers
Estimating the size of search trees by sampling with domain knowledge
IJCAI 2017
Evaluating the Power of Flexible Packet Processing for Network Resource Allocation
NSDI 2017
Event Ordering with a Generalized Model for Sieve Prediction Ranking
IJCNLP 2017
Evolving Recurrent Neural Networks That Process and Classify Raw Audio in a Streaming Fashion
Interspeech 2017
Exact MAP Inference by Avoiding Fractional Vertices
ICML 2017
Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty
RSS 2017
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
JMLR 2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
ICML 2017
Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models
IJCAI 2017
Failures of Gradient-Based Deep Learning
ICML 2017
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
AISTATS 2017
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
NeurIPS 2017
Faster Coordinate Descent via Adaptive Importance Sampling
AISTATS 2017
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation
ICML 2017
Fast Parallel Training of Neural Language Models
IJCAI 2017
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
COLT 2017
Fast rates with high probability in exp-concave statistical learning
AISTATS 2017
Fast Recursive Low-rank Tensor Learning for Regression
IJCAI 2017
Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization
IJCAI 2017
Fast SVM Trained by Divide-and-Conquer Anchors
IJCAI 2017
Fast Trajectory Optimization for Agile Quadrotor Maneuvers with a Cable-Suspended Payload
RSS 2017
FFTLasso: Large-Scale LASSO in the Fourier Domain
CVPR 2017
Filter Flow Made Practical: Massively Parallel and Lock-Free
CVPR 2017
Finite-sum Composition Optimization via Variance Reduced Gradient Descent
AISTATS 2017
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
NeurIPS 2017