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Mathematics & Optimization

16,944 papers (0 classified directly here)

Subtopics

Optimization (12,959) Mathematics (5,735) Probability (519) Statistics (265) Discrete Mathematics (15)

Papers per year

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Papers (including subtopics)

Gaussian Quadrature for Kernel Features NIPS 2017
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees NIPS 2017
Acceleration and Averaging in Stochastic Descent Dynamics NIPS 2017
Bayesian Optimization with Gradients NIPS 2017
Straggler Mitigation in Distributed Optimization Through Data Encoding NIPS 2017
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning NIPS 2017
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? NIPS 2017
Convolutional Phase Retrieval NIPS 2017
Stochastic and Adversarial Online Learning without Hyperparameters NIPS 2017
Sparse Approximate Conic Hulls NIPS 2017
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation NIPS 2017
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds NIPS 2017
Discriminative State Space Models NIPS 2017
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback NIPS 2017
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization NIPS 2017
On Frank-Wolfe and Equilibrium Computation NIPS 2017
A Screening Rule for l1-Regularized Ising Model Estimation NIPS 2017
Communication-Efficient Distributed Learning of Discrete Distributions NIPS 2017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis NIPS 2017
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling NIPS 2017
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes NIPS 2017
Online Dynamic Programming NIPS 2017
Training Deep Networks without Learning Rates Through Coin Betting NIPS 2017
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers NIPS 2017
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent NIPS 2017