Berkeley Machine Learning Tea

Machine learning tea is a weekly informal gathering for both statistics and machine learning researchers and those working in systems, AI, natural language, vision, computational biology, etc., hoping to apply statistical techniques. At the gathering, we will be entertained by a 10-minute mini-talk on an interesting application, technique, dataset, or puzzle. Snacks and drinks will be served.

When: Fridays 3-4pm

Where: RadLab lounge (Soda 467)

Format:

    3:00pm - tea and cookies

    3:15pm - talk

    3:30pm - questions + discussion

Please volunteer to give a tea talk! Contact the tea masters at (tea-organizers AT lists.eecs.berkeley.edu) if you're interested.

Subscribe to tea AT lists.eecs.berkeley.edu to get email announcements about the tea.

Why tea? We are inspired by the grand traditions at Gatsby, Toronto, and MIT.

Fall 2009 Schedule

• Sep. 11: Introductions
• Sep. 18: Paper presentations from ACL09, NIPS09, CVPR09, and OB09
• Sep. 25: Learning to Create Data-Integrating Queries [Partha Talukdar]
• Oct. 2: mlcomp launch [Percy Liang, Jake Abernethy, Alex Simma]
• Oct. 9: Learning probabilistic models using minimum probability flows (look ma, no partition functions!) [Jascha Sohl-Dickstein]
• Oct. 16: Predicting traffic patterns from GPS cell phone data [Aude Hofleitner]
• Oct. 23: Netflix Prize Redux (or how I almost won part of $1 million) [Lester Mackey]
• Oct. 30: Implicit online learning [Brian Kulis]
• Nov. 6: Open forum
• Nov. 13: Metareasoning [Nick Hay]
• Nov. 20: Blitz Learning for Complex Multi-Agent Problems [David Hall and John Blitzer]
• Nov. 27: (Thanksgiving break)
• Dec. 4:
• Dec. 11: (cancelled due NIPS)

Spring 2009 Schedule

• Jan. 30: Classifiers for Real Time Object Detection [Subhransu Maji]
• Feb. 6: Finding Solace in High Dimensions [Ariel Kleiner]
• Feb. 13: Methodology for Evaluating Models
• Feb. 20: Open forum
• Feb. 27: Deciphering Honey Bee Dances and Stock Market Swings [Emily Fox]
• Mar. 6: Hadoop for Berkeley Machine Learners [Owen O'Malley]
• Mar. 13: Hands-on with Hadoop [Andy Konwinski and Matei Zaharia]
• Mar. 20: (cancelled - concurrent talk by Andrew Gelman)
• Mar. 27: (spring break)
• Apr. 3: Probabilistic Matrix Factorization with Gaussian Processes [Raquel Urtasun]
• Apr. 10: Debate: Is learning theory useful for practioners? [Alekh Agarwal versus Aria Haghighi]
• Apr. 17: Oceanic Park: Reconstructing Extinct Protolanguages using Machine Learning [Alexandre Bouchard]
• Apr. 24: From Books to Blenders: Learning under Different Training and Test Distributions [John Blitzer]
• May 1: Understanding Human Genetic Variation: Two Statistical Problems [Sriram Sankararaman]
• May 8: BayesStore: Supporting Statistical Models in Probabilistic Databases [Daisy Wang]

Fall 2008 Schedule

• Sep. 19: Natural language grammar induction [Percy Liang]
• Sep. 26: Online learning [Jake Abernethy]
• Oct. 3: Modeling brain dynamics [Kilian Koepsell]
• Oct. 10: Causality pot-luck challenge (NIPS 2008) [Isabelle Guyon]
• Oct. 17: Why human learning matters for machine learning [Tom Griffiths]
• Oct. 24: Three Research Problems at the Intersection of Machine Learning and Computer Systems [Charles Sutton]
• Oct. 31: Machine translation [John DeNero]
• Nov. 7: Open forum
• Nov. 14: Share your productivity tools [Guillaume Obozinski and Sriram Sankararaman]
• Nov. 21: The Hahn-Banach Separation Theorem and Intel Research Berkeley, the second best research lab in the world [Ali Rahimi]
• Nov. 28: (Thanksgiving break)
• Dec. 5: Open problems in machine learning for drug discovery [Laurent Jacob]
• Dec. 12: (cancelled due NIPS)

How do I get to machine learning tea?

The RadLab is in Soda Hall, which is here. Go in the entrance on Le Roy Ave and go immediately left. You should see the door to the RadLab. If it is locked, ring the door bell and someone will come get you. After you've gone in, walk pass the conference rooms, and the lounge should be on the left (there might be a curtain).