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John C Duchi

John Duchi

A little about me: (Just so you know) I am currently a PhD candidate in computer science at Berkeley, where I started in the fall of 2008. I work in the Statistical Artificial Intelligence Lab (SAIL) under the supervision of Mike Jordan. Before this, I was an undergrad and a masters student at Stanford University working with Daphne Koller in her research group, DAGS. I was also a Resident Assistant in Cedro, which might well be the best all-freshman dorm at Stanford. My little brother, Andrew Duchi, is a sophomore this year at Stanford. I have also worked at at Google.

Contact info: [Visit]

Recipe Book: [Draft]

Publications

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, Advances in Neural Information Processing Systems (NIPS 2006). [pdf]

Classes I TA

CS227, Reasoning Methods in Artificial Intelligence, Spring 2006, Spring 2007, taught by Pandurang Nayak.

CS228, Probabilistic Models in Artificial Intelligence, Winter 2007, taught by Daphne Koller.

A Few Class Papers, Potentially Useful Notes, and Scribing

Note that none of these are guaranteed in any way to be correct, so forgive me if they are not. I just sometimes like to derive things that I may find useful later.

Notes on concentration bounds and probability inequalities, for fun. [pdf]

Derivations for Linear Algebra and Optimization, for fun. [pdf]

Notes on some matrix properties, for fun. [pdf]