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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)

An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models ACML 2010
Finite-sample Analysis of Bellman Residual Minimization ACML 2010
Learning the Structure of Deep Sparse Graphical Models AISTATS 2010
Optimal Allocation Strategies for the Dark Pool Problem AISTATS 2010
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels AISTATS 2010
Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010
Deterministic Bayesian inference for the $p*$ model AISTATS 2010
Kernel Partial Least Squares is Universally Consistent AISTATS 2010
Why are DBNs sparse? AISTATS 2010
Focused Belief Propagation for Query-Specific Inference AISTATS 2010
Parametric Herding AISTATS 2010
Mass Fatality Incident Identification based on nuclear DNA evidence AISTATS 2010
On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010
Improving posterior marginal approximations in latent Gaussian models AISTATS 2010
Impossibility Theorems for Domain Adaptation AISTATS 2010
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010
Bayesian variable order Markov models AISTATS 2010
Nonparametric Bayesian Matrix Factorization by Power-EP AISTATS 2010
Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010
Semi-Supervised Learning via Generalized Maximum Entropy AISTATS 2010
Posterior distributions are computable from predictive distributions AISTATS 2010
Variational methods for Reinforcement Learning AISTATS 2010
Understanding the difficulty of training deep feedforward neural networks AISTATS 2010
Regret Bounds for Gaussian Process Bandit Problems AISTATS 2010
Sufficient covariates and linear propensity analysis AISTATS 2010