Stats, Optimization, & Machine Learning Seminar - Hunaid Contractor and Peter Shaffery

Nov. 29, 2016

Event Description: Hunaid Contractor, Department of Applied Mathematics, University of Colorado Boulder Peter Shaffery, Department of Applied Mathematics, University of Colorado Boulder Seminar meets in DUAN G2B21. Location Information: Main Campus - Duane Physics ( View Map ) 2000 COLORADO AV Boulder, CO Contact Information: Name: Ian Cunningham Phone: 303-492-4668...

Stats, Optimization, & Machine Learning Seminar - Osman Malik, Andreas Wachter, and Zhishen Huang

Nov. 15, 2016

Event Description: Osman Malik, Department of Applied Mathematics, University of Colorado Boulder Andreas Wachter; Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Zhishen Huang, Department of Applied Mathematics, University of Colorado Boulder Location Information: Main Campus - Duane Physics ( View Map ) 2000 COLORADO AV Boulder,...

Stats, Optimization, & Machine Learning Seminar - Dan Milroy

Nov. 8, 2016

Event Description: Dan Milroy, Research Computing, University of Colorado Boulder Quantum Machine Learning Seminar meets in DUAN G2B21. Location Information: Main Campus - Duane Physics ( View Map ) 2000 COLORADO AV Boulder, CO Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu

Stats, Optimization, & Machine Learning Seminar

Nov. 1, 2016

Event Description: Seminar course covering advanced topics in Statistics, Optimization, and Machine Learning. Seminar meets in DUAN G2B21. Location Information: Main Campus - Duane Physics ( View Map ) 2000 COLORADO AV Boulder, CO Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu

Stats, Optimization, & Machine Learning Seminar - Adam Bloniarz

Oct. 25, 2016

Event Description: Adam Bloniarz; Department of Statistics; Google, Boulder, CO Lasso adjustments of treatment effects in randomized experiments We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting...

Stats, Optimization, & Machine Learning Seminar - Anastasios Kyrillidis

Oct. 18, 2016

Event Description: SEMINAR MEETS IN MATH 100 THIS WEEK Anastasios Kyrillidis, The Wireless Networking and Communications Group (WNCG), University of Texas at Austin Finding low-rank solutions via the Burer-Monteiro approach, efficiently and provably A low rank matrix can be described as the outer product of two tall matrices, where the...

Stats, Optimization, & Machine Learning Seminar -

Oct. 11, 2016

Event Description: Seminar course covering advanced topics in Statistics, Optimization, and Machine Learning. Seminar meets in DUAN G2B21. Location Information: Main Campus - Duane Physics ( View Map ) 2000 COLORADO AV Boulder, CO Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu

Stats, Optimization, & Machine Learning Seminar - Kamalika Chaudhuri

Oct. 4, 2016

Event Description: Kamalika Chaudhuri; Department of Computer Science and Engineering; University of California, San Diego ​ Challenges in Privacy-Preserving Data Analysis Machine learning algorithms increasingly work with sensitive information on individuals, and hence the problem of privacy-preserving data analysis -- how to design data analysis algorithms that operate on the...

Stats, Optimization, & Machine Learning Seminar - Bo Waggoner

Sept. 27, 2016

Event Description: Bo Waggoner, Department of Computer Science, University of Pennsylvania What Dice Are These? You are handed a die with n sides and asked to describe some property of it. Is it fair or biased? If biased, what is its entropy? Can you write down its distribution over sides,...

Stats, Optimization, & Machine Learning Seminar - Jake Abernethy

Sept. 20, 2016

Event Description: Jacob Abernethy; Department of Electrical Engineering and Computer Science; University of Michigan, Ann Arbor On the equivalence of simulated annealing and interior point path following for optimization A well-studied deterministic algorithmic technique for convex optimization is the class of so-called "interior point methods" of Nesterov and Nemirovski, which...

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