← Optimization & Theory
Machine Learning › Optimization & Theory ›

Optimization

14207 directly classified papers

Papers per year

Papers

Scalable kernels for graphs with continuous attributes NIPS 2013
Mixed Optimization for Smooth Functions NIPS 2013
Adaptive Step-Size for Policy Gradient Methods NIPS 2013
Adaptive Submodular Maximization in Bandit Setting NIPS 2013
Optimization, Learning, and Games with Predictable Sequences NIPS 2013
Robust Data-Driven Dynamic Programming NIPS 2013
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima NIPS 2013
Actor-Critic Algorithms for Risk-Sensitive MDPs NIPS 2013
A Stability-based Validation Procedure for Differentially Private Machine Learning NIPS 2013
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning NIPS 2013
Regret based Robust Solutions for Uncertain Markov Decision Processes NIPS 2013
Projected Natural Actor-Critic NIPS 2013
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NIPS 2013
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements NIPS 2013
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings NIPS 2013
A New Convex Relaxation for Tensor Completion NIPS 2013
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions NIPS 2013
Learning Chordal Markov Networks by Constraint Satisfaction NIPS 2013
Designed Measurements for Vector Count Data NIPS 2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transport NIPS 2013
Convex Two-Layer Modeling NIPS 2013
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses NIPS 2013
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints NIPS 2013
Machine Teaching for Bayesian Learners in the Exponential Family NIPS 2013
Learning Multiple Models via Regularized Weighting NIPS 2013