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Optimization & Theory

36,874 papers (3 classified directly here)

Subtopics

Optimization (14,983) Bayesian Inference (5,566) Learning Theory (5,334) Theory (4,955) Statistical Learning (4,117) Neural Network Optimization (3,649) Stochastic Processes (2,667) Loss Functions (1,162) Distributed Learning (1,135) Stochastic Methods (1,118) Online Algorithms (806) Statistics (684) Information Theory (318) Generalization (220) Sparse Optimization (158) Sample Complexity (99) Game Theory (30) Robust Optimization (17) Optimal Transport (14) Approximation Algorithms (10) Distributed Optimization (8) Convex Optimization (8)

Papers per year

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Papers (including subtopics)

Deep Learning without Poor Local Minima NIPS 2016
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression NIPS 2016
Learning and Forecasting Opinion Dynamics in Social Networks NIPS 2016
Agnostic Estimation for Misspecified Phase Retrieval Models NIPS 2016
The Generalized Reparameterization Gradient NIPS 2016
Semiparametric Differential Graph Models NIPS 2016
Learning to learn by gradient descent by gradient descent NIPS 2016
Fast Active Set Methods for Online Spike Inference from Calcium Imaging NIPS 2016
Optimal Learning for Multi-pass Stochastic Gradient Methods NIPS 2016
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition NIPS 2016
Natural-Parameter Networks: A Class of Probabilistic Neural Networks NIPS 2016
Minimizing Quadratic Functions in Constant Time NIPS 2016
A Strategy for Ranking Optimization Methods using Multiple Criteria AUTOML 2016
A Brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning Without Human Intervention AUTOML 2016
Bayesian optimization for automated model selection AUTOML 2016
Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry AUTOML 2016
Towards Automatically-Tuned Neural Networks AUTOML 2016
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning AUTOML 2016
Parameter-Free Convex Learning through Coin Betting AUTOML 2016
Multiple Kernel Learning with Data Augmentation ACML 2016
Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization ACML 2016
Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers ACML 2016
Learnability of Non-I.I.D. ACML 2016
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees ACML 2016
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays ACML 2016