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This workshop addresses questions at the intersection of discrete and combinatorial optimization and machine learning.
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 impractical, intractable, or both.
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 on how to discover, exploit, and deploy such structure.
Machine learning, algorithms, discrete mathematics and combinatorics as well as applications in computer vision, speech, NLP, biology and network analysis are all active areas of research, each with an increasingly large body of foundational knowledge. The workshop aims to ask questions that enable communication across these fields.
Author Information
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.
Andreas Krause (ETHZ)
Stefanie Jegelka (MIT)
S Thomas McCormick (Sauder School of Business, UBC)
Sebastian Nowozin (DeepMind)
Yaron Singer (Harvard University)
Dhruv Batra (FAIR (Meta) / Georgia Tech)
Volkan Cevher (EPFL)
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2017 : Invited Talk 2 »
Dhruv Batra -
2017 : Invited talk: Towards Safe Bayesian Optimization »
Andreas Krause -
2017 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Abhishek Das · Satwik Kottur · Stefan Lee · Mateusz Malinowski · Olivier Pietquin · Devi Parikh · Dhruv Batra · Aaron Courville · Jeremie Mary -
2017 Workshop: Discrete Structures in Machine Learning »
Yaron Singer · Jeff A Bilmes · Andreas Krause · Stefanie Jegelka · Amin Karbasi -
2017 Poster: Interactive Submodular Bandit »
Lin Chen · Andreas Krause · Amin Karbasi -
2017 Poster: Minimizing a Submodular Function from Samples »
Eric Balkanski · Yaron Singer -
2017 Poster: The Numerics of GANs »
Lars Mescheder · Sebastian Nowozin · Andreas Geiger -
2017 Poster: Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach »
Slobodan Mitrovic · Ilija Bogunovic · Ashkan Norouzi-Fard · Jakub M Tarnawski · Volkan Cevher -
2017 Spotlight: The Numerics of GANs »
Lars Mescheder · Sebastian Nowozin · Andreas Geiger -
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: Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data »
Joel A Tropp · Alp Yurtsever · Madeleine Udell · Volkan Cevher -
2017 Poster: Robust Optimization for Non-Convex Objectives »
Robert S Chen · Brendan Lucier · Yaron Singer · Vasilis Syrgkanis -
2017 Spotlight: Differentiable Learning of Submodular Functions »
Josip Djolonga · Andreas Krause -
2017 Oral: Robust Optimization for Non-Convex Objectives »
Robert S Chen · Brendan Lucier · Yaron Singer · Vasilis Syrgkanis -
2017 Poster: Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model »
Jiasen Lu · Anitha Kannan · Jianwei Yang · Devi Parikh · Dhruv Batra -
2017 Poster: Phase Transitions in the Pooled Data Problem »
Jonathan Scarlett · Volkan Cevher -
2017 Poster: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms »
Yatao Bian · Kfir Levy · Andreas Krause · Joachim M Buhmann -
2017 Poster: Stabilizing Training of Generative Adversarial Networks through Regularization »
Kevin Roth · Aurelien Lucchi · Sebastian Nowozin · Thomas Hofmann -
2017 Poster: Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization »
Ahmet Alacaoglu · Quoc Tran Dinh · Olivier Fercoq · Volkan Cevher -
2017 Poster: The Importance of Communities for Learning to Influence »
Eric Balkanski · Nicole Immorlica · Yaron Singer -
2017 Poster: Stochastic Submodular Maximization: The Case of Coverage Functions »
Mohammad Karimi · Mario Lucic · Hamed Hassani · Andreas Krause -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Training Generative Neural Samplers using Variational Divergence »
Sebastian Nowozin -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Poster: An Efficient Streaming Algorithm for the Submodular Cover Problem »
Ashkan Norouzi-Fard · Abbas Bazzi · Ilija Bogunovic · Marwa El Halabi · Ya-Ping Hsieh · Volkan Cevher -
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: f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization »
Sebastian Nowozin · Botond Cseke · Ryota Tomioka -
2016 Poster: Maximization of Approximately Submodular Functions »
Thibaut Horel · Yaron Singer -
2016 Poster: Hierarchical Question-Image Co-Attention for Visual Question Answering »
Jiasen Lu · Jianwei Yang · Dhruv Batra · Devi Parikh -
2016 Poster: The Power of Optimization from Samples »
Eric Balkanski · Aviad Rubinstein · Yaron Singer -
2016 Poster: Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles »
Stefan Lee · Senthil Purushwalkam · Michael Cogswell · Viresh Ranjan · David Crandall · Dhruv Batra -
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: Deep Submodular Functions: Definitions and Learning »
Brian W Dolhansky · Jeffrey A Bilmes -
2016 Poster: Stochastic Three-Composite Convex Minimization »
Alp Yurtsever · Bang Cong Vu · Volkan Cevher -
2016 Poster: DISCO Nets : DISsimilarity COefficients Networks »
Diane Bouchacourt · Pawan K Mudigonda · Sebastian Nowozin -
2015 : Safe Exploration for Bayesian Optimization »
Andreas Krause -
2015 : Visual Question Answering »
Dhruv Batra -
2015 Poster: Learnability of Influence in Networks »
Harikrishna Narasimhan · David Parkes · Yaron Singer -
2015 Poster: Information-theoretic lower bounds for convex optimization with erroneous oracles »
Yaron Singer · Jan Vondrak -
2015 Spotlight: Information-theoretic lower bounds for convex optimization with erroneous oracles »
Yaron Singer · Jan Vondrak -
2015 Poster: SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals »
Qing Sun · Dhruv Batra -
2015 Poster: Preconditioned Spectral Descent for Deep Learning »
David Carlson · Edo Collins · Ya-Ping Hsieh · Lawrence Carin · Volkan Cevher -
2015 Poster: A Universal Primal-Dual Convex Optimization Framework »
Alp Yurtsever · Quoc Tran Dinh · Volkan Cevher -
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 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 Poster: Divide-and-Conquer Learning by Anchoring a Conical Hull »
Tianyi Zhou · Jeffrey A Bilmes · Carlos Guestrin -
2014 Poster: Constrained convex minimization via model-based excessive gap »
Quoc Tran-Dinh · Volkan Cevher -
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: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · 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 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: On the Convergence Rate of Decomposable Submodular Function Minimization »
Robert Nishihara · Stefanie Jegelka · Michael Jordan -
2014 Poster: Time--Data Tradeoffs by Aggressive Smoothing »
John J Bruer · Joel A Tropp · Volkan Cevher · Stephen Becker -
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: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Poster: Decision Jungles: Compact and Rich Models for Classification »
Jamie Shotton · Toby Sharp · Pushmeet Kohli · Sebastian Nowozin · John Winn · Antonio Criminisi -
2013 Poster: High-Dimensional Gaussian Process Bandits »
Josip Djolonga · Andreas Krause · Volkan Cevher -
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 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: Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions »
Rishabh K Iyer · Stefanie Jegelka · Jeffrey A Bilmes -
2013 Tutorial: Deep Mathematical Properties of Submodularity with Applications to Machine Learning »
Jeffrey A Bilmes -
2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar -
2012 Poster: Multiple Choice Learning: Learning to Produce Multiple Structured Outputs »
Abner Guzmán-Rivera · Dhruv Batra · Pushmeet Kohli -
2012 Poster: Active Learning of Multi-Index Function Models »
Hemant Tyagi · Volkan Cevher -
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: Optimization for Machine Learning »
Suvrit Sra · Stephen Wright · Sebastian Nowozin -
2011 Workshop: Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity »
Greg Shakhnarovich · Dhruv Batra · Brian Kulis · Kilian Q Weinberger -
2011 Oral: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
2011 Poster: Fast approximate submodular minimization »
Stefanie Jegelka · Hui Lin · Jeffrey A Bilmes -
2011 Poster: Scalable Training of Mixture Models via Coresets »
Dan Feldman · Matthew Faulkner · Andreas Krause -
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: Online Submodular Set Cover, Ranking, and Repeated Active Learning »
Andrew Guillory · Jeffrey A Bilmes -
2011 Spotlight: Online Submodular Set Cover, Ranking, and Repeated Active Learning »
Andrew Guillory · Jeffrey A Bilmes -
2011 Poster: Higher-Order Correlation Clustering for Image Segmentation »
Sungwoong Kim · Sebastian Nowozin · Pushmeet Kohli · Chang D. D Yoo -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Stephen Wright -
2010 Spotlight: Efficient Minimization of Decomposable Submodular Functions »
Peter G Stobbe · Andreas Krause -
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: Near-Optimal Bayesian Active Learning with Noisy Observations »
Daniel Golovin · Andreas Krause · Debajyoti Ray -
2009 Workshop: Manifolds, sparsity, and structured models: When can low-dimensional geometry really help? »
Richard Baraniuk · Volkan Cevher · Mark A Davenport · Piotr Indyk · Bruno Olshausen · Michael B Wakin -
2009 Workshop: Optimization for Machine Learning »
Sebastian Nowozin · Suvrit Sra · S.V.N Vishwanthan · Stephen Wright -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
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 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 Poster: Learning with Compressible Priors »
Volkan Cevher -
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 -
2008 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Vishwanathan S V N -
2008 Poster: Sparse Signal Recovery Using Markov Random Fields »
Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk -
2008 Spotlight: Sparse Signal Recovery Using Markov Random Fields »
Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk -
2007 Spotlight: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2007 Poster: Selecting Observations against Adversarial Objectives »
Andreas Krause · H. Brendan McMahan · Carlos Guestrin · Anupam Gupta -
2006 Demonstration: The Vocal Joystick »
James Landay · Richard Wright · Kelley Kilanski · Xiao Li · Jon Malkin · Jeffrey A Bilmes -
2006 Poster: Multi-dynamic Bayesian Networks »
Karim Filali · Jeffrey A Bilmes