Microsoft Research
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Machine Learning Seminar
Date Location Speaker Title
4/22/2013  99/1927  Quoc Le  Scaling Deep Learning to 10,000 Cores and Beyond 
3/27/2013  99/1919   Abhishek Kumar  Algorithms for Near-Separable Nonnegative Matrix Factorization 
3/26/2013  99/1927   Dong Yu  Deep Neural Network for Speech Recognition – Insights and Advances 
3/13/2013  99/1915   Manik Varma  Multi-Label Learning with Millions of Labels for Query Recommendation 
1/29/2013   99/1919   Qiang Liu  Belief Propagation Algorithms for Crowdsourcing 

 

2011-2012 

 

10/25/2011  99/1927  Daniel Hsu Efficient algorithms for high-dimensional bandit problems
11/8/2011  99/1927 

Ran Gilad-Bachrach

 The Median Hypothesis 
11/16/2011  99/4800  Marina Meila  Consensus finding, exponential models, and infinite rankings 
11/22/2011  99/1927  Andre Martins Structured Prediction in NLP: Dual Decomposition and Structured Sparsity
12/6/2011  99/1927  Alekh Agarwal  Learning and stochastic optimization with non-i.i.d. data
1/3/2012     99/1927  Li Deng  Deep learning for Information Processing 
1/17/2012  99/1927  David McAllester  Generalization Bounds and Consistency for Latent-Structural Probit and Ramp Loss 
1/31/2012  99/1927  Murali Haran 

Gaussian processes for inference with implicit likelihoods

2/8/2012  99/1927  Qiaozhu Mei The Foreseer: Integrative Retrieval and Mining of information in Online Communities 
2/14/2012  99/1927  Chong Wang Hierarchical Bayesian modeling: efficient inference and applications
3/13/2012  99/1927  Xi Chen 

Optimization for General Structured Sparse Learning

3/20/2012  99/1927  Jonathan Goldstein  Temporal Analytics on Big Data for Web Advertising 
3/21/2012  99/1915 Anima Anandkumar  High-Dimensional Estimation via Graphical Approaches: Methods and Guarantees  
3/27/2012  99/1927  Antony Joseph   Achieving information-theoretic limits in high-dimensional regression
4/3/2012  99/1927  Hau-tieng Wu   Vector Diffusion Maps, Connection Laplacian and their applications 
4/10/2012  99/1927  Christopher Ré  

Going Hogwild!: Parallelizing Incremental Gradient Methods and Matrix Mean Inequalities 

4/11/2012  99/1927  Lihong Li  Machine Learning in the Bandit Setting: Algorithms, Evaluation, and Case Studies 
5/1/2012  99/1927  Christian Shelton  The case for continuous time 
5/4/2012  99/1927  Ben Recht   The Convex Geometry of Inverse Problems
5/8/2012  99/1927  Yucheng Low  GraphLab2: Distributed Graph-Parallel Computation on Natural GraphsGraphLab2: Distributed Graph-Parallel Computation on Natural Graphs
5/14/2012  99/1927 Lise Getoor   Collective Graph Identification
5/15/2012  99/1927  John Langford  A Reliable Effective Terascale Linear Learning System 
6/12/2012 99/1927 Kilian Weinberger   mSDA: A fast and easy-to-use way to improve bag-of-words features
6/19/2012  99/1927  Miro Dudik  Tractable market making in combinatorial prediction markets
7/3/2012  99/1927  Abhradeep Guha Thakurta  Differentially Private Learning on Large, Online and High-dimensional Data 
7/18/2012  99/3042  James Bergstra  Grid Search is a Bad Hyper-parameter Optimization Algorithm
8/7/2012  99/1927  Karthik Sridharan TBD 
8/20/2012  99/1927  Saeed Amizadeh  Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation