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Machine Learning

121,146 papers (10 classified directly here)

Subtopics

Learning Types (52,936) Core Methods (48,729) Optimization & Theory (36,874) Application Areas (31,658) Bayesian & Probabilistic (3,963) Learning Paradigms (2,054)

Papers per year

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Papers (including subtopics)

PAC-Bayesian Theory for Transductive Learning AISTATS 2014
Random Bayesian networks with bounded indegree AISTATS 2014
Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs AISTATS 2014
Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems AISTATS 2014
Doubly Aggressive Selective Sampling Algorithms for Classification AISTATS 2014
Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus AISTATS 2014
Sparsity and the Truncated $l^2$-norm AISTATS 2014
Efficient Distributed Topic Modeling with Provable Guarantees AISTATS 2014
Pan-sharpening with a Bayesian nonparametric dictionary learning model AISTATS 2014
Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014
Bayesian Logistic Gaussian Process Models for Dynamic Networks AISTATS 2014
Avoiding pathologies in very deep networks AISTATS 2014
Efficient Inference for Complex Queries on Complex Distributions AISTATS 2014
Bayesian Switching Interaction Analysis Under Uncertainty AISTATS 2014
Robust learning of inhomogeneous PMMs AISTATS 2014
Fully-Automatic Bayesian Piecewise Sparse Linear Models AISTATS 2014
Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014
Robust Stochastic Principal Component Analysis AISTATS 2014
Bayesian Nonparametric Poisson Factorization for Recommendation Systems AISTATS 2014
Efficiently Enforcing Diversity in Multi-Output Structured Prediction AISTATS 2014
Learning and Evaluation in Presence of Non-i.i.d. Label Noise AISTATS 2014
Analytic Long-Term Forecasting with Periodic Gaussian Processes AISTATS 2014
On Estimating Causal Effects based on Supplemental Variables AISTATS 2014
Non-Asymptotic Analysis of Relational Learning with One Network AISTATS 2014
Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014