John Duchi's Webpage

John C Duchi

John Duchi

A little about me: I am currently a PhD candidate in computer science at Berkeley, where I started in the fall of 2008. My research interests are a bit eclectic, and they span computation, statistics, optimization, and machine learning; if you like any of these, we can probably find something interesting to chat about. I work in the Statistical Artificial Intelligence Lab (SAIL) under the joint supervision of Mike Jordan and Martin Wainwright. I obtained my master's degree (MA) in statistics in Fall 2012. I was initially supported by an NDSEG fellowship, and until recently was supported by Facebook, who generously awarded me a Facebook Fellowship. Before this, I was an undergrad and a masters student at Stanford University working with Daphne Koller in her research group, DAGS. I also spend some time at Google Research (once upon a time I was also a software engineer there), where I had (and continue to have) the great fortune to work with Yoram Singer. I grew up in the delightfully friendly midwest, and I was once a triathlete, but now I just enjoy a spot of running or biking here and there. Last, but certainly not least, I got married to my wonderful wife Emily in the summer of 2008. (Here is a slightly more formal bio in the third-person.)

Curriculum Vitae: [pdf]

Contact info: [Visit]


Publications

Clicking the publication title will give an abstract and publication information.

Preprints/In Preparation

Optimal rates for zero-order optimization: the power of two function evaluations, John C. Duchi, Michael I. Jordan, Martin J. Wainwright, and Andre Wibisono. [pdf]

Distance-based and continuum Fano inequalities with applications to statistical estimation, John C. Duchi, and Martin J. Wainwright. [pdf]

Local Privacy, Data Processing Inequalities, and Minimax Rates, John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. [pdf, slides]

Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates, Yuchen Zhang, John C. Duchi, and Martin J. Wainwright. [pdf]

Oracle Inequalities for Computationally Adaptive Model Selection, Alekh Agarwal, Peter L. Bartlett, and John C. Duchi. [pdf]

Journal Articles

Privacy Aware Learning, John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. Journal of the Association for Computing Machinery, 2014, to appear. [pdf]

The Asymptotics of Ranking Algorithms, John C. Duchi, Lester Mackey, Michael I. Jordan. Annals of Statistics 41(5):2292--2323, 2013. [pdf]

Communication-Efficient Algorithms for Statistical Optimization, Yuchen Zhang, John C. Duchi, and Martin Wainwright. Journal of Machine Learning Research 14(Nov):3321--3363, 2013. [pdf]

The Generalization Ability of Online Algorithms for Dependent Data, Alekh Agarwal and John C. Duchi. IEEE Transactions on Information Theory (2013). [pdf]

Ergodic Mirror Descent, John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan. SIAM Journal on Optimization (SIOPT), 2012. [pdf]

Randomized Smoothing for Stochastic Optimization, John Duchi, Peter L. Bartlett, and Martin Wainwright. SIAM Journal on Optimization (SIOPT), 2012. [pdf]

Dual Averaging for Distributed Optimization: Convergence and Network Scaling, John Duchi, Alekh Agarwal, and Martin Wainwright. IEEE Transactions on Automatic Control (March 2012). [pdf]

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, John Duchi, Elad Hazan, and Yoram Singer. Journal of Machine Learning Research (JMLR 2011). [pdf]

Efficient Online and Batch Learning using Forward Backward Splitting, John Duchi and Yoram Singer. Journal of Machine Learning Research (JMLR 2009) and Neural Information Processing Systems (NIPS 2009). [pdf]

Conference Proceedings

Estimation, Optimization, and Parallelism when Data is Sparse, John C. Duchi, Michael I. Jordan, and Brendan McMahan. Neural Information Processing Systems (NIPS 2013). [pdf]

Information-theoretic lower bounds for distributed statistical estimation with communication constraints, Yuchen Zhang, John C. Duchi, Michael I. Jordan, and Martin Wainwright. Neural Information Processing Systems (NIPS 2013). [pdf]

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation, John C. Duchi, Michael I. Jordan, and Martin Wainwright. Neural Information Processing Systems (NIPS 2013). [pdf coming soon]

Local Privacy and Statistical Minimax Rates, John C. Duchi, Michael I. Jordan, and Martin Wainwright. 54th Annual Symposium on Foundations of Computer Science (FOCS 2013). [pdf]

Divide and Conquer Kernel Ridge Regression, Yuchen Zhang, John C. Duchi, and Martin Wainwright. Conference on Learning Theory (COLT 2013). [pdf]

Privacy Aware Learning, John C. Duchi, Michael I. Jordan, and Martin Wainwright. Neural Information Processing Systems (NIPS 2012). [pdf]

Communication-Efficient Algorithms for Statistical Optimization, Yuchen Zhang, John C. Duchi, and Martin Wainwright. Neural Information Processing Systems (NIPS 2012). [pdf]

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods, John C. Duchi, Michael I. Jordan, Martin Wainwright, and Andre Wibisono. Neural Information Processing Systems (NIPS 2012). [pdf]

Randomized Smoothing for (Parallel) Stochastic Optimization, John Duchi, Peter L. Bartlett, and Martin Wainwright. International Conference on Machine Learning (ICML 2012) . Presented but not included in proceedings. [pdf]

Distributed Delayed Stochastic Optimization, Alekh Agarwal and John Duchi. Neural Information Processing Systems (NIPS 2011). [pdf] [Long pdf]

Ergodic Subgradient Descent, John Duchi Alekh Agarwal, Mikael Johansson, Michael I. Jordan. Allerton Conference on Communications, Control, and Computing 2011. [pdf]

Oracle Inequalities for Computationally Budgeted Model Selection, Alekh Agarwal, John Duchi, Peter L. Bartlett, Clement Levrard. Conference on Learning Theory (COLT 2011). [pdf]

Distributed Dual Averaging in Networks, John Duchi, Alekh Agarwal, and Martin Wainwright. Neural Information Processing Systems (NIPS 2010). [pdf]

On the Consistency of Ranking Algorithms, John Duchi, Lester Mackey, and Michael Jordan. International Conference on Machine Learning (ICML 2010). [pdf] Winner of best student paper award.

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, John Duchi, Elad Hazan, and Yoram Singer. Conference on Learning Theory (COLT 2010). [pdf]

Composite Objective Mirror Descent, John Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari. Conference on Learning Theory (COLT 2010). [pdf]

Efficient Learning using Forward Backward Splitting, John Duchi and Yoram Singer. Neural Information Processing Systems (NIPS 2009). [pdf]

Boosting with Structural Sparsity, John Duchi and Yoram Singer. International Conference on Machine Learning (ICML 2009). [pdf] [Long pdf]

Efficient Projections onto the L1-Ball for Learning in High Dimensions, John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. International Conference on Machine Learning (ICML 2008). [pdf]

Constrained Approximate Maximum Entropy Learning of Markov Random Fields, Varun Ganapathi, David Vickrey, John Duchi, and Daphne Koller. Conference on Uncertainty in Artificial Intelligence (UAI 2008). [pdf]

Projected Subgradient Methods for Learning Sparse Gaussians, John Duchi, Stephen Gould and Daphne Koller. Conference on Uncertainty in Artificial Intelligence (UAI 2008). [pdf]

Using Combinatorial Optimization within Max-Product Belief Propagation, John Duchi, Danny Tarlow, Gal Elidan, and Daphne Koller. Neural Information Processing Systems (NIPS 2006). [pdf]