FilterBoost: Regression and Classification on Large Datasets. - Joseph K. Bradley; Robert E. Schapire (NIPS, 2007)

Learning Tree Conditional Random Fields. - Joseph K. Bradley; Carlos Guestrin (ICML, 2010)

Parallel Coordinate Descent for L1-Regularized Loss Minimization. - Joseph K. Bradley; Aapo Kyrola; Danny Bickson; Carlos Guestrin (ICML, 2011)

Sample Complexity of Composite Likelihood. - Joseph K. Bradley; Carlos Guestrin (AISTATS, 2012)

Learning Large-Scale Conditional Random Fields. - Joseph K. Bradley (2013)

The SPRIGHT algorithm for robust sparse Hadamard Transforms. - Xiao Li 022; Joseph Kurata Bradley; Sameer Pawar; Kannan Ramchandran (ISIT, 2014)

Parallel Double Greedy Submodular Maximization. - Xinghao Pan; Stefanie Jegelka; Joseph E. Gonzalez; Joseph K. Bradley; Michael I. Jordan (NIPS, 2014)

When is it Better to Compare than to Score? - Nihar B. Shah; Sivaraman Balakrishnan; Joseph K. Bradley; Abhay Parekh; Kannan Ramchandran; Martin J. Wainwright (CoRR, 2014)

Spark SQL: Relational Data Processing in Spark. - Michael Armbrust; Reynold S. Xin; Cheng Lian; Yin Huai; Davies Liu; Joseph K. Bradley; Xiangrui Meng; Tomer Kaftan; Michael J. Franklin; Ali Ghodsi 002; Matei Zaharia (SIGMOD Conference, 2015)

SPRIGHT: A Fast and Robust Framework for Sparse Walsh-Hadamard Transform. - Xiao Li 022; Joseph K. Bradley; Sameer Pawar; Kannan Ramchandran (CoRR, 2015)

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. - Firas Abuzaid; Joseph K. Bradley; Feynman T. Liang; Andrew Feng; Lee Yang; Matei Zaharia; Ameet Talwalkar (NIPS, 2016)

MLlib: Machine Learning in Apache Spark. - Xiangrui Meng; Joseph K. Bradley; Burak Yavuz; Evan Randall Sparks; Shivaram Venkataraman; Davies Liu; Jeremy Freeman; D. B. Tsai; Manish Amde; Sean Owen; Doris Xin; Reynold Xin; et al. (J. Mach. Learn. Res., 2016)

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. - Nihar B. Shah; Sivaraman Balakrishnan; Joseph K. Bradley; Abhay Parekh; Kannan Ramchandran; Martin J. Wainwright (J. Mach. Learn. Res., 2016)