maximum likelihood estimation

BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Probabilistic Label Trees for Efficient Large Scale Image Classification CVPR 2013 Spectral methods for neural characterization using generalized quadratic models NeurIPS 2013 Extrinsic Calibration from Per-Sensor Egomotion RSS 2012 Iterative ranking from pair-wise comparisons NeurIPS 2012 Learning with Target Prior NeurIPS 2012 Majorization for CRFs and Latent Likelihoods NeurIPS 2012 Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions NeurIPS 2012 Asymptotic Theory for Linear-Chain Conditional Random Fields AISTATS 2011 Bayesian Spike-Triggered Covariance Analysis NeurIPS 2011 Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs NeurIPS 2011 Generative Modeling for Maximizing Precision and Recall in Information Visualization AISTATS 2011 Relational Learning with One Network: An Asymptotic Analysis AISTATS 2011 -Nested Symmetric Distributions JMLR 2010 On the Convexity of Latent Social Network Inference NeurIPS 2010 Sparse Inverse Covariance Selection via Alternating Linearization Methods NeurIPS 2010 Adaptive relative bundle adjustment RSS 2009 Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors JMLR 2009 Sparse and Locally Constant Gaussian Graphical Models NeurIPS 2009 Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models NeurIPS 2009 A Convex Upper Bound on the Log-Partition Function for Binary Distributions NeurIPS 2008 Characterizing neural dependencies with copula models NeurIPS 2008 Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform NeurIPS 2008 Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex NeurIPS 2008 Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NeurIPS 2008