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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 Gate Recurrent Neural Network ACML 2016
Proper Inner Product with Mean Displacement for Gaussian Noise Invariant ICA ACML 2016
Geometry-aware stationary subspace analysis ACML 2016
EcoICA: Skewness-based ICA via Eigenvectors of Cumulant Operator ACML 2016
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching AISTATS 2016
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics AISTATS 2016
Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models AISTATS 2016
Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums AISTATS 2016
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models AISTATS 2016
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AISTATS 2016
A Linearly-Convergent Stochastic L-BFGS Algorithm AISTATS 2016
No Regret Bound for Extreme Bandits AISTATS 2016
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models AISTATS 2016
Time-Varying Gaussian Process Bandit Optimization AISTATS 2016
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies AISTATS 2016
Nearly Optimal Classification for Semimetrics AISTATS 2016
Latent Point Process Allocation AISTATS 2016
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings AISTATS 2016
Bayesian Generalised Ensemble Markov Chain Monte Carlo AISTATS 2016
A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning AISTATS 2016
Optimal Statistical and Computational Rates for One Bit Matrix Completion AISTATS 2016
PAC-Bayesian Bounds based on the Rényi Divergence AISTATS 2016
Simple and Scalable Constrained Clustering: a Generalized Spectral Method AISTATS 2016