Machine Learning and Optimization Group (India)

The Machine Learning and Optimization Group of Microsoft Research pushes the state of the art in machine learning. Our work spans the space from theoretical foundations of machine learning, to new machine learning systems and algorithms, to helping our partner product groups apply machine learning to large and complex tasks.

Machine Learning Seminar

Date Location Speaker Title
5/6/2014 99/1927 Guy Lebanon Local Low-Rank Matrix Approximation
4/22/2014 99/1927 Junming Yin TBD
3/26/2014 99/1927 Scott Sanner Data-driven Decision-making
3/24/2014 99/1927 Jonathan Huang Data Driven Student Feedback For MOOCs: Global Scale Education for the 21st century
2/18/2014 99/1927 Csaba Szepesvari Sparse Stochastic Bandits
2/6/2014 99/1915 Eric Xing On The Algorithmic and System Interface of BIG LEARNING
1/31/2014 99/1927 Bin Yu Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations
1/21/2014 99/1915 Sebastian Bubeck The linear bandit problem
1/14/2014 99/1915 Robert Schapire Explaining AdaBoost

2013

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

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