Journal of Machine Learning Research
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Publication Venue For
- Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes. 23. 2022
- Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. 23. 2022
- Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. 23. 2022
- Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters. 23. 2022
- Spatial Multivariate Trees for Big Data Bayesian Regression. 23. 2022
- A review of robot learning for manipulation: Challenges, representations, and algorithms. 22. 2021
- Bayesian distance clustering. 22:1-27. 2021
- Bayesian time-aligned factor analysis of paired multivariate time series.. 22:250. 2021
- Estimating Uncertainty Intervals from Collaborating Networks 2021
- FLAME: A fast large-scale almost matching exactly approach to causal inference. 22. 2021
- Model linkage selection for cooperative learning. 22. 2021
- Regulating greed over time in multi-armed bandits. 22. 2021
- Soft tensor regression. 22:1-53. 2021
- Understanding how dimension reduction tools work: An empirical approach to deciphering T-SNE, UMAP, TriMap, and PaCMAP for data visualization. 22. 2021
- Nonparametric graphical model for counts.. 21:229. 2020
- Bayesian closed surface fitting through tensor products. 21:1-26. 2020
- Learning optimized risk scores. 20. 2019
- All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.. 20:177. 2019
- Scaling up data augmentation MCMC via calibration. 19. 2018
- Scalable Bayes via barycenter in Wasserstein space. 19:1-35. 2018
- Submatrix localization via message passing. 18:1-52. 2018
- Learning certifiably optimal rule lists for categorical data. 18:1-78. 2018
- Robust and scalable bayes via a median of subset posterior measures. 18:1-40. 2017
- A Bayesian framework for learning rule sets for interpretable classification. 18:1-37. 2017
- Bayesian tensor regression. 18:1-31. 2017
- Analyzing tensor power method dynamics in overcomplete regime. 18:1-40. 2017
- Bayesian learning of dynamic multilayer networks. 18:1-29. 2017
- Structure-Leveraged Methods in Breast Cancer Risk Prediction.. 17. 2016
- Bayesian graphical models for multivariate functional data. 17:1-27. 2016
- The factorized self-controlled case series method: An approach for estimating the effects of many drugs on many outcomes. 17. 2016
- Compressed Gaussian process for manifold regression. 17. 2016
- Structure-leveraged methods in breast cancer risk prediction. 17. 2016
- Structure-leveraged methods in breast cancer risk prediction. 17. 2016
- Electronic health record analysis via deep poisson factor models. 17. 2016
- Improving structure MCMC for Bayesian networks through Markov Blanket resampling. 17:1-20. 2016
- Bayesian nonparametric covariance regression. 16:2501-2542. 2015
- Learning transformations for clustering and classification. 16:187-225. 2015
- Tensor decompositions for learning latent variable models. 15:2773-2832. 2014
- Statistical-computational tradeoffs in planted problems and submatrix localization with a growing number of clusters and submatrices. 17:882-938. 2014
- A tensor approach to learning mixed membership community models. 15:2239-2312. 2014
- Improving prediction from dirichlet process mixtures via enrichment. 15:1041-1071. 2014
- Locally adaptive factor processes for multivariate time series. 15:1493-1522. 2014
- New algorithms for learning incoherent and overcomplete dictionaries. 35:779-806. 2014
- SMERED: A Bayesian approach to graphical record linkage and de-duplication. 33:922-930. 2014
- Learning theory analysis for association rules and sequential event prediction. 14:3441-3492. 2013
- Multivariate Convex Regression with Adaptive Partitioning. 14:3261-3294. 2013
- Multivariate convex regression with adaptive partitioning. 14:3153-3188. 2013
- The rate of convergence of AdaBoost. 14:2315-2347. 2013
- Machine learning with operational costs. 14:1989-2028. 2013
- A tensor spectral approach to learning mixed membership community models. 30:867-881. 2013
- Transfer in reinforcement learning via shared features. 13:1333-1371. 2012
- Beta-negative binomial process and poisson factor analysis. 22:1462-1471. 2012
- Dependent hierarchical beta process for image interpolation and denoising. 15:883-891. 2011
- Discussion of "a conditional game for comparing approximations". 15:72-73. 2011
- Preface to the proceedings of AISTATS 2011. 15:1-2. 2011
- On equivalence relationships between classification and ranking algorithms. 12:2905-2929. 2011
- Sparse linear identifiable multivariate modeling. 12:863-905. 2011
- Logistic Stick-Breaking Process.. 12:203-239. 2011
- Detecting weak but hierarchically-structured patterns in networks. 9:749-756. 2010
- Learning non-stationary dynamic bayesian networks. 11:3647-3680. 2010
- Preface to the Proceedings of AISTATS 2011. 9:1-2. 2010
- Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.. 11:1771-1798. 2010
- Classification with Incomplete Data Using Dirichlet Process Priors.. 11:3269-3311. 2010
- Online learning for matrix factorization and sparse coding. 11:19-60. 2010
- Exploiting product distributions to identify relevant variables of correlation immune functions. 10:2375-2411. 2009
- Margin-based ranking and an equivalence between AdaBoost and RankBoost. 10:2193-2232. 2009
- The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list. 10:2233-2271. 2009
- Multi-task reinforcement learning in partially observable stochastic environments. 10:1131-1186. 2009
- Characterizing the function space for bayesian kernel models. 8:1769-1797. 2007
- Multi-task learning for classification with Dirichlet process priors. 8:35-63. 2007
- The dynamics of AdaBoost: Cyclic behavior and convergence of margins. 5:1557-1595. 2004
- The minimum error minimax probability machine. 5:1253-1286. 2004
- Lossless online Bayesian bagging. 5:143-151. 2004
- Efficient approaches for escaping higher order saddle points in non-convex optimization 2016
- Competing with the empirical risk minimizer in a single pass 2015
- Escaping from saddle points: Online stochastic gradient for tensor decomposition 2015
- Escaping the local minima via simulated annealing: Optimization of approximately convex functions 2015
- Falling rule lists 2015
- Learning overcomplete latent variable models through tensor methods 2015
- Simple, efficient, and neural algorithms for sparse coding 2015
- WASP: Scalable Bayes via barycenters of subset posteriors 2015
- Average case analysis of high-dimensional block-sparse recovery and regression for arbitrary designs 2014
- Bayesian logistic Gaussian process models for dynamic networks 2014
- Stochastic regret minimization via Thompson Sampling 2014
- Bayesian learning of joint distributions of objects 2013
- Diagonal orthant multinomial probit models 2013
- Hierarchical latent dictionaries for models of brain activation 2012
- Sequential event prediction with association rules 2011
- The rate of convergence of AdaBoost 2011
- ILP: A short look back and a longer look forward 2004