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Solving optimization problems with ultimately discrete solutions is becoming increasingly important in machine learning. At the core of statistical machine learning is to make inferences from data, and when the variables underlying the data are discrete, both the tasks of inferring the model from data as well as performing predictions using the estimated model are inherently discrete optimization problems. Many of these optimization problems are notoriously hard. As a result, abundant and steadily increasing amounts of data -- despite being statistically beneficial -- quickly render standard off-the-shelf optimization procedures either intractable, or at the very least impractical.
However, while many problems are hard in the worst case, the problems of practical interest are often much more well-behaved, or are well modeled by assuming properties that make them so. Indeed, many discrete problems in machine learning can possess beneficial structure; such structure has been an important ingredient in many successful (approximate) solution strategies. Examples include the marginal polytope, which is determined by the graph structure of the model, or sparsity that makes it possible to handle high dimensions. Symmetry and exchangeability are further exploitable characteristics. In addition, functional properties such as submodularity, a discrete analog of convexity, are proving to be useful to an increasing number of machine learning problems.
One of the primary goals of this workshop is to provide a platform for exchange of ideas -- between machine learning, algorithms, discrete mathematics and combinatorics as well as application areas of computer vision, speech, NLP, biology and network analysis -- on how to discover, exploit, and deploy such structure.
Author Information
Stefanie Jegelka (MIT)
Andreas Krause (ETHZ)
Pradeep Ravikumar (Carnegie Mellon University)
Kazuo Murota (University of Tokyo)
Jeffrey A Bilmes (University of Washington, Seattle)
Jeffrey A. Bilmes is a professor at the Department of Electrical and Computer Engineering at the University of Washington, Seattle Washington. He is also an adjunct professor in Computer Science & Engineering and the department of Linguistics. Prof. Bilmes is the founder of the MELODI (MachinE Learning for Optimization and Data Interpretation) lab here in the department. Bilmes received his Ph.D. from the Computer Science Division of the department of Electrical Engineering and Computer Science, University of California in Berkeley and a masters degree from MIT. He was also a researcher at the International Computer Science Institute, and a member of the Realization group there. Prof. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Prof. Bilmes was, along with Andrew Ng, one of the two UAI (Conference on Uncertainty in Artificial Intelligence) program chairs (2009) and then the general chair (2010). He was also a workshop chair (2011) and the tutorials chair (2014) at NIPS/NeurIPS (Neural Information Processing Systems), and is a regular senior technical chair at NeurIPS/NIPS since then. He was an action editor for JMLR (Journal of Machine Learning Research). Prof. Bilmes's primary interests lie in statistical modeling (particularly graphical model approaches) and signal processing for pattern classification, speech recognition, language processing, bioinformatics, machine learning, submodularity in combinatorial optimization and machine learning, active and semi-supervised learning, and audio/music processing. He is particularly interested in temporal graphical models (or dynamic graphical models, which includes HMMs, DBNs, and CRFs) and ways in which to design efficient algorithms for them and design their structure so that they may perform as better structured classifiers. He also has strong interests in speech-based human-computer interfaces, the statistical properties of natural objects and natural scenes, information theory and its relation to natural computation by humans and pattern recognition by machines, and computational music processing (such as human timing subtleties). He is also quite interested in high performance computing systems, computer architecture, and software techniques to reduce power consumption. Prof. Bilmes has also pioneered (starting in 2003) the development of submodularity within machine learning, and he received a best paper award at ICML 2013, a best paper award at NIPS 2013, and a best paper award at ACMBCB in 2016, all in this area. In 2014, Prof. Bilmes also received a most influential paper in 25 years award from the International Conference on Supercomputing, given to a paper on high-performance matrix optimization. Prof. Bilmes has authored the graphical models toolkit (GMTK), a dynamic graphical-model based software system widely used in speech, language, bioinformatics, and human-activity recognition.
Yisong Yue (Disney Research)
Michael Jordan (UC Berkeley)
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Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Spotlight: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Oral: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Poster: Safe Model-based Reinforcement Learning with Stability Guarantees »
Felix Berkenkamp · Matteo Turchetta · Angela Schoellig · Andreas Krause -
2017 Poster: Differentiable Learning of Submodular Functions »
Josip Djolonga · Andreas Krause -
2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Spotlight: Differentiable Learning of Submodular Functions »
Josip Djolonga · Andreas Krause -
2017 Spotlight: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Poster: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms »
Yatao Bian · Kfir Levy · Andreas Krause · Joachim M Buhmann -
2017 Poster: Non-convex Finite-Sum Optimization Via SCSG Methods »
Lihua Lei · Cheng Ju · Jianbo Chen · Michael Jordan -
2017 Poster: Stochastic Submodular Maximization: The Case of Coverage Functions »
Mohammad Karimi · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: Variational Inference in Mixed Probabilistic Submodular Models »
Josip Djolonga · Sebastian Tschiatschek · Andreas Krause -
2016 Poster: Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation »
Ilija Bogunovic · Jonathan Scarlett · Andreas Krause · Volkan Cevher -
2016 Poster: Cyclades: Conflict-free Asynchronous Machine Learning »
Xinghao Pan · Maximilian Lam · Stephen Tu · Dimitris Papailiopoulos · Ce Zhang · Michael Jordan · Kannan Ramchandran · Christopher Ré · Benjamin Recht -
2016 Poster: Unsupervised Domain Adaptation with Residual Transfer Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Cooperative Graphical Models »
Josip Djolonga · Stefanie Jegelka · Sebastian Tschiatschek · Andreas Krause -
2016 Poster: Fast and Provably Good Seedings for k-Means »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 Oral: Fast and Provably Good Seedings for k-Means »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 Poster: Safe Exploration in Finite Markov Decision Processes with Gaussian Processes »
Matteo Turchetta · Felix Berkenkamp · Andreas Krause -
2016 Poster: Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain »
Ian En-Hsu Yen · Xiangru Huang · Kai Zhong · Ruohan Zhang · Pradeep Ravikumar · Inderjit Dhillon -
2016 Poster: Deep Submodular Functions: Definitions and Learning »
Brian W Dolhansky · Jeffrey A Bilmes -
2015 : Safe Exploration for Bayesian Optimization »
Andreas Krause -
2015 Poster: Fast Classification Rates for High-dimensional Gaussian Generative Models »
Tianyang Li · Adarsh Prasad · Pradeep Ravikumar -
2015 Poster: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Spotlight: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
Nikhil Rao · Hsiang-Fu Yu · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Variational Consensus Monte Carlo »
Maxim Rabinovich · Elaine Angelino · Michael Jordan -
2015 Poster: Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs »
Vidyashankar Sivakumar · Arindam Banerjee · Pradeep Ravikumar -
2015 Poster: Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent »
Ian En-Hsu Yen · Kai Zhong · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: Consistent Multilabel Classification »
Oluwasanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Poster: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause -
2015 Poster: Sampling from Probabilistic Submodular Models »
Alkis Gotovos · Hamed Hassani · Andreas Krause -
2015 Poster: Submodular Hamming Metrics »
Jennifer Gillenwater · Rishabh K Iyer · Bethany Lusch · Rahul Kidambi · Jeffrey A Bilmes -
2015 Poster: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2015 Spotlight: Closed-form Estimators for High-dimensional Generalized Linear Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Spotlight: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause -
2015 Oral: Sampling from Probabilistic Submodular Models »
Alkis Gotovos · Hamed Hassani · Andreas Krause -
2015 Spotlight: Submodular Hamming Metrics »
Jennifer Gillenwater · Rishabh K Iyer · Bethany Lusch · Rahul Kidambi · Jeffrey A Bilmes -
2015 Poster: Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications »
Kai Wei · Rishabh K Iyer · Shengjie Wang · Wenruo Bai · Jeffrey A Bilmes -
2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Discrete Optimization in Machine Learning »
Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
2014 Poster: Divide-and-Conquer Learning by Anchoring a Conical Hull »
Tianyi Zhou · Jeffrey A Bilmes · Carlos Guestrin -
2014 Poster: QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Stephen Becker · Peder A Olsen -
2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Efficient Sampling for Learning Sparse Additive Models in High Dimensions »
Hemant Tyagi · Bernd Gärtner · Andreas Krause -
2014 Poster: From MAP to Marginals: Variational Inference in Bayesian Submodular Models »
Josip Djolonga · Andreas Krause -
2014 Poster: On the Information Theoretic Limits of Learning Ising Models »
Rashish Tandon · Karthikeyan Shanmugam · Pradeep Ravikumar · Alex Dimakis -
2014 Poster: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · Michael Jordan -
2014 Poster: Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space »
Ian En-Hsu Yen · Ting-Wei Lin · Shou-De Lin · Pradeep Ravikumar · Inderjit Dhillon -
2014 Spotlight: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Spotlight: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets »
Adarsh Prasad · Stefanie Jegelka · Dhruv Batra -
2014 Poster: Efficient Partial Monitoring with Prior Information »
Hastagiri P Vanchinathan · Gábor Bartók · Andreas Krause -
2014 Poster: Learning Mixtures of Submodular Functions for Image Collection Summarization »
Sebastian Tschiatschek · Rishabh K Iyer · Haochen Wei · Jeffrey A Bilmes -
2014 Poster: Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators »
Kai Zhong · Ian En-Hsu Yen · Inderjit Dhillon · Pradeep Ravikumar -
2014 Spotlight: Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets »
Adarsh Prasad · Stefanie Jegelka · Dhruv Batra -
2014 Session: Oral Session 1 »
Jeffrey A Bilmes -
2014 Poster: A Representation Theory for Ranking Functions »
Harsh H Pareek · Pradeep Ravikumar -
2014 Poster: Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs »
David I Inouye · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings »
Ian En-Hsu Yen · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Elementary Estimators for Graphical Models »
Eunho Yang · Aurelie Lozano · Pradeep Ravikumar -
2014 Poster: On the Convergence Rate of Decomposable Submodular Function Minimization »
Robert Nishihara · Stefanie Jegelka · Michael Jordan -
2014 Poster: Weakly-supervised Discovery of Visual Pattern Configurations »
Hyun Oh Song · Yong Jae Lee · Stefanie Jegelka · Trevor Darrell -
2014 Session: Tutorial Session B »
Jeffrey A Bilmes -
2014 Session: Tutorial Session B »
Jeffrey A Bilmes -
2014 Session: Tutorial Session B »
Jeffrey A Bilmes -
2013 Workshop: Machine Learning for Sustainability »
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao -
2013 Workshop: Bayesian Optimization in Theory and Practice »
Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré -
2013 Poster: Conditional Random Fields via Univariate Exponential Families »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: High-Dimensional Gaussian Process Bandits »
Josip Djolonga · Andreas Krause · Volkan Cevher -
2013 Poster: On Poisson Graphical Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · Zhandong Liu -
2013 Poster: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Oral: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar · Russell Poldrack -
2013 Session: Oral Session 10 »
Michael Jordan -
2013 Poster: Dirty Statistical Models »
Eunho Yang · Pradeep Ravikumar -
2013 Poster: A Comparative Framework for Preconditioned Lasso Algorithms »
Fabian L Wauthier · Nebojsa Jojic · Michael Jordan -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Poster: Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints »
Rishabh K Iyer · Jeffrey A Bilmes -
2013 Oral: Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints »
Rishabh K Iyer · Jeffrey A Bilmes -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Poster: Large Scale Distributed Sparse Precision Estimation »
Huahua Wang · Arindam Banerjee · Cho-Jui Hsieh · Pradeep Ravikumar · Inderjit Dhillon -
2013 Poster: Learning with Noisy Labels »
Nagarajan Natarajan · Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan -
2013 Poster: Distributed Submodular Maximization: Identifying Representative Elements in Massive Data »
Baharan Mirzasoleiman · Amin Karbasi · Rik Sarkar · Andreas Krause -
2013 Poster: Reflection methods for user-friendly submodular optimization »
Stefanie Jegelka · Francis Bach · Suvrit Sra -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Streaming Variational Bayes »
Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan -
2013 Poster: Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions »
Rishabh K Iyer · Stefanie Jegelka · Jeffrey A Bilmes -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2013 Tutorial: Deep Mathematical Properties of Submodularity with Applications to Machine Learning »
Jeffrey A Bilmes -
2012 Workshop: Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty »
Jonathan How · Lawrence Carin · John Fisher III · Michael Jordan · Alborz Geramifard -
2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu -
2012 Poster: Ancestor Sampling for Particle Gibbs »
Fredrik Lindsten · Michael Jordan · Thomas Schön -
2012 Oral: Graphical Models via Generalized Linear Models »
Eunho Yang · Pradeep Ravikumar · Genevera I Allen · zhandong Liu -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models »
Ke Jiang · Brian Kulis · Michael Jordan -
2012 Poster: A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation »
Cho-Jui Hsieh · Inderjit Dhillon · Pradeep Ravikumar · Arindam Banerjee -
2012 Poster: Submodular Bregman Divergences with Applications »
Rishabh K Iyer · Jeffrey A Bilmes -
2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Oral: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Bayesian Bias Mitigation for Crowdsourcing »
Fabian L Wauthier · Michael Jordan -
2011 Poster: Divide-and-Conquer Matrix Factorization »
Lester W Mackey · Ameet S Talwalkar · Michael Jordan -
2011 Poster: Fast approximate submodular minimization »
Stefanie Jegelka · Hui Lin · Jeffrey A Bilmes -
2011 Poster: On Learning Discrete Graphical Models using Greedy Methods »
Ali Jalali · Christopher C Johnson · Pradeep Ravikumar -
2011 Poster: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Spotlight: On Learning Discrete Graphical Models using Greedy Methods »
Ali Jalali · Christopher C Johnson · Pradeep Ravikumar -
2011 Poster: Contextual Gaussian Process Bandit Optimization »
Andreas Krause · Cheng Soon Ong -
2011 Poster: Crowdclustering »
Ryan G Gomes · Peter Welinder · Andreas Krause · Pietro Perona -
2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon -
2011 Poster: Online Submodular Set Cover, Ranking, and Repeated Active Learning »
Andrew Guillory · Jeffrey A Bilmes -
2011 Poster: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation »
Cho-Jui Hsieh · Matyas A Sustik · Inderjit Dhillon · Pradeep Ravikumar -
2011 Session: Oral Session 5 »
Pradeep Ravikumar -
2011 Spotlight: Online Submodular Set Cover, Ranking, and Repeated Active Learning »
Andrew Guillory · Jeffrey A Bilmes -
2011 Poster: Nearest Neighbor based Greedy Coordinate Descent »
Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Workshop: Robust Statistical Learning »
Pradeep Ravikumar · Constantine Caramanis · Sujay Sanghavi -
2010 Session: Oral Session 14 »
Pradeep Ravikumar -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Invited Talk: Statistical Inference of Protein Structure and Function »
Michael Jordan -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Spotlight: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Oral: A Dirty Model for Multi-task Learning »
Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan -
2010 Spotlight: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Poster: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Discriminative Clustering by Regularized Information Maximization »
Ryan G Gomes · Andreas Krause · Pietro Perona -
2010 Poster: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
2010 Poster: Near-Optimal Bayesian Active Learning with Noisy Observations »
Daniel Golovin · Andreas Krause · Debajyoti Ray -
2010 Poster: A Dirty Model for Multi-task Learning »
Ali Jalali · Pradeep Ravikumar · Sujay Sanghavi · Chao Ruan -
2010 Poster: Heavy-Tailed Process Priors for Selective Shrinkage »
Fabian L Wauthier · Michael Jordan -
2010 Poster: Random Conic Pursuit for Semidefinite Programming »
Ariel Kleiner · ali rahimi · Michael Jordan -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
2009 Poster: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2009 Spotlight: Information-theoretic lower bounds on the oracle complexity of convex optimization »
Alekh Agarwal · Peter Bartlett · Pradeep Ravikumar · Martin J Wainwright -
2009 Oral: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2009 Poster: Submodularity Cuts and Applications »
Yoshinobu Kawahara · Kiyohito Nagano · Koji Tsuda · Jeffrey A Bilmes -
2009 Poster: Label Selection on Graphs »
Andrew Guillory · Jeffrey A Bilmes -
2009 Poster: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu -
2009 Spotlight: Submodularity Cuts and Applications »
Yoshinobu Kawahara · Kiyohito Nagano · Koji Tsuda · Jeffrey A Bilmes -
2009 Spotlight: Online Learning of Assignments »
Matthew Streeter · Daniel Golovin · Andreas Krause -
2009 Oral: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
Sahand N Negahban · Pradeep Ravikumar · Martin J Wainwright · Bin Yu -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Poster: Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification »
Amarnag Subramanya · Jeffrey A Bilmes -
2009 Spotlight: Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification »
Amarnag Subramanya · Jeffrey A Bilmes -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2008 Oral: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Poster: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Spotlight: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
Pradeep Ravikumar · Vincent Vu · Bin Yu · Thomas Naselaris · Kendrick Kay · Jack Gallant -
2008 Poster: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
2008 Spotlight: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Poster: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2008 Poster: Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE »
Pradeep Ravikumar · Garvesh Raskutti · Martin J Wainwright · Bin Yu -
2008 Spotlight: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Spotlight: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Poster: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Poster: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Spotlight: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft -
2006 Demonstration: The Vocal Joystick »
James Landay · Richard Wright · Kelley Kilanski · Xiao Li · Jon Malkin · Jeffrey A Bilmes -
2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
Martin J Wainwright · Pradeep Ravikumar · John Lafferty -
2006 Spotlight: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
Martin J Wainwright · Pradeep Ravikumar · John Lafferty -
2006 Poster: Multi-dynamic Bayesian Networks »
Karim Filali · Jeffrey A Bilmes