A relatively up-to-date list of publications:

  • Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer. “Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions” Preprint 2017 [arXiv]

  • Xin Wang, Fisher Yu, Zi-Yi Dou, Joseph E. Gonzalez. “SkipNet: Learning Dynamic Routing in Convolutional Networks.” Preprint 2017 [arXiv]

  • Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez “IDK Cascades: Fast Deep Learning by Learning not to Overthink.” Preprint 2017 [arXiv]

  • Ion Stoica, Dawn Song, Raluca Ada Popa, David A. Patterson, Michael W. Mahoney, Randy H. Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David E. Culler, Pieter Abbeel. “Electrical Engineering and Computer Sciences University of California at Berkeley” Berkeley Technical Report 2017. [Paper]

  • Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Burton Smith, and Randy H. Katz. “Selecting the Best VM Across Multiple Public Clouds: A Data-driven Performance Modeling Approach.” SoCC ‘17. [Paper]

  • Francois W. Belletti, Evan R. Sparks, Michael J. Franklin, Alexandre M. Bayen, and Joseph E. Gonzalez. “Random Projection Design for Scalable Implicit Smoothing of Randomly Observed Stochastic Processes.” AIStats ’17. [Paper]

  • Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. “Clipper: A low-latency online prediction serving system.” NSDI ‘17. [Paper]

  • Wenting Zheng, Ankur Dave, Jethro G. Beekman, Raluca Ada Popa, Joseph E. Gonzalez, and Ion Stoica. “Opaque: An oblivious and encrypted distributed analytics platform.” NSDI ‘17. [Paper]

  • Joseph M. Hellerstein, Vikram Sreekanti, Joseph E. Gonzalez, Sudhansku Arora, Arka Bhattacharyya, Shirshanka Das, Akon Dey, Mark Donsky, Gabriel Fierro, Sreyashi Nag, Krishna Ramachandran, Chang She, Eric Sun, Carl Steinbach, Venkat Subramanian. “Establishing Common Ground with Data Context.” CIDR, 2017. [Paper]

  • Francois W. Belletti, Evan R. Sparks, Michael J. Franklin, Alexandre M. Bayen, Joseph E. Gonzalez. “Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies” (under review) arXiv, 2016. [Paper]

  • Ankur Dave, Alekh Jindal, Li Erran Li, Reynold Xin, Joseph Gonzalez, and Matei Zaharia. “GraphFrames: An Integrated API for Mixing Graph and Relational Queries.” In SIGMOD Grades Workshop, 2016. [Paper]

  • Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz. “Multi-task Learning for Straggler Avoiding Predictive Job Scheduling.” In Journal of Machine Learning Research (JMLR ’16), 2016.[Paper]

  • Neeraja J. Yadwadkar, Bharath Hariharan, Joseph Gonzalez and Randy Katz (2015). “Faster Jobs in Distributed Data Processing using Multi-Task Learning” Conference: SIAM International Conference on Data Mining (SDM15). [Paper]

  • Dan Crankshaw, Peter Bailis, Joseph Gonzalez, Haoyuan Li, Zhao Zhang, Michael Franklin, Ali Ghodsi, and Michael Jordan (2015). “The missing piece in complex analytics: Low latency, scalable model management and serving with Velox.” Conference: Conference on Innovative Data Systems Research (CIDR). [Paper]

  • Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, and Michael I. Jordan (2014). “Parallel double greedy submodular maximization.” Advances in Neural Information Processing Systems (NIPS). [Paper] [code]

  • Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, Ion Stoica (2014). “GraphX: Graph Processing in a Distributed Dataflow Framework.Proceedings of Operating Systems Design and Implementation (OSDI). [Paper]

  • Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, and Michael I. Jordan (2013). “Optimistic concurrency control for distributed unsupervised learning..” Advances in Neural Information Processing Systems (NIPS) 26, 2013.. [Paper]

  • Evan Sparks, Ameet Talwalkar, Virginia Smith, Xinghao Pan, Joseph Gonzalez, Tim Kraska, Michael I. Jordan, and Michael J. Franklin (2013). “MLI: An API for distributed machine learning..” IEEE International Conference on Data Mining (ICDM).. [Paper]

  • Reynold Xin, Joseph Gonzalez, Michael Franklin, Ion Stoica (2013). “GraphX: A Resilient Distributed Graph System on Spark..” SIGMOD 2013 GRADES Workshop.. [Paper]

  • Joseph Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, Carlos Guestrin (2012). “PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs.” Proceedings of Operating Systems Design and Implementation (OSDI). [GraphLab2 (PowerGraph)] [abs/bib] [pdf]

  • Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin and Joseph M. Hellerstein (2012). “Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud.” Proceedings of Very Large Data Bases (PVLDB). [code release] [abs/bib] [pdf]

  • Amr Ahmed, Mohamed Aly, Joseph Gonzalez, Shravan Narayanamurthy, Alex Smola (2012). “Scalable Inference in Latent Variable Models.” Conference on Web Search and Data Mining (WSDM). [bibtex] [pdf]

  • Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin (2011). “Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.” In Artificial Intelligence and Statistics (AISTATS). [code release] [abs/bib] [pdf] [pptx]

  • Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein (2010). “GraphLab: A New Parallel Framework for Machine Learning.” Conference on Uncertainty in Artificial Intelligence (UAI). [code release] [abs/bib] [pdf]

  • Joseph Gonzalez, Yucheng Low, Carlos Guestrin (2010). “Parallel Inference on Large Factor Graphs.” Book chapter in Scalable MachineLearning.

  • Joseph Gonzalez, Yucheng Low, Carlos Guestrin, David O`Hallaron (2009). “Distributed Parallel Inference on Large Factor Graphs.” Conference on Uncertainty in Artificial Intelligence (UAI). [abs/bib] [pdf] [pptx]

  • Joseph Gonzalez, Yucheng Low, and Carlos Guestrin (2009). “Residual Splash for Optimally Parallelizing Belief Propagation.” In Artificial Intelligence and Statistics (AISTATS). [abs/bib] [pdf] [pptx]