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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)

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes AISTATS 2013
Nyström Approximation for Large-Scale Determinantal Processes AISTATS 2013
Further Optimal Regret Bounds for Thompson Sampling AISTATS 2013
Distributed and Adaptive Darting Monte Carlo through Regenerations AISTATS 2013
Ultrahigh Dimensional Feature Screening via RKHS Embeddings AISTATS 2013
Convex Collective Matrix Factorization AISTATS 2013
Efficiently Sampling Probabilistic Programs via Program Analysis AISTATS 2013
Computing the M Most Probable Modes of a Graphical Model AISTATS 2013
A simple criterion for controlling selection bias AISTATS 2013
Evidence Estimation for Bayesian Partially Observed MRFs AISTATS 2013
Why Steiner-tree type algorithms work for community detection AISTATS 2013
Deep Gaussian Processes AISTATS 2013
ODE parameter inference using adaptive gradient matching with Gaussian processes AISTATS 2013
Dynamic Copula Networks for Modeling Real-valued Time Series AISTATS 2013
Data-driven covariate selection for nonparametric estimation of causal effects AISTATS 2013
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension AISTATS 2013
DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes AISTATS 2013
A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions AISTATS 2013
Exact Learning of Bounded Tree-width Bayesian Networks AISTATS 2013
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables AISTATS 2013
Structure Learning of Mixed Graphical Models AISTATS 2013
Learning Markov Networks With Arithmetic Circuits AISTATS 2013
Fast Near-GRID Gaussian Process Regression AISTATS 2013
Thompson Sampling in Switching Environments with Bayesian Online Change Detection AISTATS 2013
A Last-Step Regression Algorithm for Non-Stationary Online Learning AISTATS 2013