John Duchi's Webpage
John C 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
junior this year at Stanford. I have also worked at at Google, where I had
(and continue to have) the great fortune to work with
Yoram Singer. Last, but certainly not least, I got married to my
wonderful wife Emily in the summer
of 2008.
Contact info: [Visit]
Recipe Book: [Draft]
Publications (in chronological order)
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).
Boosting with Structural Sparsity,
John Duchi and
Yoram Singer. International Conference on Machine Learning
(ICML 2009).
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).
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).
Projected Subgradient Methods for Learning Sparse Gaussians,
John Duchi,
Stephen Gould and
Daphne Koller.
Conference on Uncertainty in Artificial Intelligence (UAI 2008).
Using Combinatorial Optimization within Max-Product Belief
Propagation,
John Duchi,
Danny Tarlow,
Gal Elidan, and
Daphne Koller.
Neural Information Processing Systems (NIPS 2006).
Classes I have TAed
Berkeley EE127a, Introduction to Optimization, Spring 2009,
taught by
Laurent El Ghaoui (Berkeley).
Stanford CS227, Reasoning
Methods in Artificial Intelligence, Spring 2006, Spring
2007, taught by Pandurang Nayak (Stanford).
Stanford 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]
