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Author Information
Tianyi Zhou (University of Maryland, College Park)

Tianyi Zhou (https://tianyizhou.github.io) is a tenure-track assistant professor of computer science at the University of Maryland, College Park. He received his Ph.D. from the school of computer science & engineering at the University of Washington, Seattle. His research interests are in machine learning, optimization, and natural language processing (NLP). His recent works study curriculum learning that can combine high-level human learning strategies with model training dynamics to create a hybrid intelligence. The applications include semi/self-supervised learning, robust learning, reinforcement learning, meta-learning, ensemble learning, etc. He published >80 papers and is a recipient of the Best Student Paper Award at ICDM 2013 and the 2020 IEEE Computer Society TCSC Most Influential Paper Award.
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.
Carlos Guestrin (Apple & University of Washington)
More from the Same Authors
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2021 Spotlight: Constrained Robust Submodular Partitioning »
Shengjie Wang · Tianyi Zhou · Chandrashekhar Lavania · Jeff A Bilmes -
2022 Spotlight: Federated Learning from Pre-Trained Models: A Contrastive Learning Approach »
Yue Tan · Guodong Long · Jie Ma · LU LIU · Tianyi Zhou · Jing Jiang -
2022 Spotlight: Lightning Talks 3A-1 »
Shu Ding · Wanxing Chang · Jiyang Guan · Mouxiang Chen · Guan Gui · Yue Tan · Shiyun Lin · Guodong Long · Yuze Han · Wei Wang · Zhen Zhao · Ye Shi · Jian Liang · Chenghao Liu · Lei Qi · Ran He · Jie Ma · Zemin Liu · Xiang Li · Hoang Tuan · Luping Zhou · Zhihua Zhang · Jianling Sun · Jingya Wang · LU LIU · Tianyi Zhou · Lei Wang · Jing Jiang · Yinghuan Shi -
2022 Spotlight: Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach »
Kaiwen Yang · Yanchao Sun · Jiahao Su · Fengxiang He · Xinmei Tian · Furong Huang · Tianyi Zhou · Dacheng Tao -
2022 Poster: Federated Learning from Pre-Trained Models: A Contrastive Learning Approach »
Yue Tan · Guodong Long · Jie Ma · LU LIU · Tianyi Zhou · Jing Jiang -
2022 Poster: Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach »
Kaiwen Yang · Yanchao Sun · Jiahao Su · Fengxiang He · Xinmei Tian · Furong Huang · Tianyi Zhou · Dacheng Tao -
2022 Poster: Retrospective Adversarial Replay for Continual Learning »
Lilly Kumari · Shengjie Wang · Tianyi Zhou · Jeff A Bilmes -
2021 Poster: Constrained Robust Submodular Partitioning »
Shengjie Wang · Tianyi Zhou · Chandrashekhar Lavania · Jeff A Bilmes -
2021 Poster: Class-Disentanglement and Applications in Adversarial Detection and Defense »
Kaiwen Yang · Tianyi Zhou · Yonggang Zhang · Xinmei Tian · Dacheng Tao -
2021 Poster: CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum »
Shuang Ao · Tianyi Zhou · Guodong Long · Qinghua Lu · Liming Zhu · Jing Jiang -
2020 Session: Orals & Spotlights Track 32: Optimization »
Hamed Hassani · Jeffrey A Bilmes -
2020 Poster: Curriculum Learning by Dynamic Instance Hardness »
Tianyi Zhou · Shengjie Wang · Jeffrey A Bilmes -
2019 : Jeffrey Bilmes »
Jeff A Bilmes -
2019 Poster: Curriculum-guided Hindsight Experience Replay »
Meng Fang · Tianyi Zhou · Yali Du · Lei Han · Zhengyou Zhang -
2019 Poster: On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks »
Sunil Thulasidasan · Gopinath Chennupati · Jeffrey A Bilmes · Tanmoy Bhattacharya · Sarah Michalak -
2019 Poster: Learning to Propagate for Graph Meta-Learning »
LU LIU · Tianyi Zhou · Guodong Long · Jing Jiang · Chengqi Zhang -
2018 Poster: Diverse Ensemble Evolution: Curriculum Data-Model Marriage »
Tianyi Zhou · Shengjie Wang · Jeffrey A Bilmes -
2018 Poster: Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions »
Wenruo Bai · William Stafford Noble · Jeffrey A Bilmes -
2018 Poster: Learning to Optimize Tensor Programs »
Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy -
2018 Spotlight: Learning to Optimize Tensor Programs »
Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy -
2018 Poster: Training Deep Models Faster with Robust, Approximate Importance Sampling »
Tyler Johnson · Carlos Guestrin -
2017 Workshop: Discrete Structures in Machine Learning »
Yaron Singer · Jeff A Bilmes · Andreas Krause · Stefanie Jegelka · Amin Karbasi -
2016 : Invited talk, Carlos Guestrin »
Carlos Guestrin -
2016 Poster: Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization »
Tyler Johnson · Carlos Guestrin -
2016 Poster: Deep Submodular Functions: Definitions and Learning »
Brian W Dolhansky · Jeffrey A Bilmes -
2015 Poster: Submodular Hamming Metrics »
Jennifer Gillenwater · Rishabh K Iyer · Bethany Lusch · Rahul Kidambi · Jeffrey A Bilmes -
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: 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: Learning Mixtures of Submodular Functions for Image Collection Summarization »
Sebastian Tschiatschek · Rishabh K Iyer · Haochen Wei · Jeffrey A Bilmes -
2014 Session: Oral Session 1 »
Jeffrey A Bilmes -
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: 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: 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: 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 Demonstration: GraphLab: A Framework For Machine Learning in the Cloud »
Yucheng Low · Haijie Gu · Carlos Guestrin -
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 Poster: Fast approximate submodular minimization »
Stefanie Jegelka · Hui Lin · Jeffrey A Bilmes -
2011 Poster: Linear Submodular Bandits and their Application to Diversified Retrieval »
Yisong Yue · Carlos Guestrin -
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 -
2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka -
2010 Poster: Evidence-Specific Structures for Rich Tractable CRFs »
Anton Chechetka · Carlos Guestrin -
2010 Poster: Inference with Multivariate Heavy-Tails in Linear Models »
Danny Bickson · Carlos Guestrin -
2009 Workshop: Learning with Orderings »
Tiberio Caetano · Carlos Guestrin · Jonathan Huang · Risi Kondor · Guy Lebanon · Marina Meila -
2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
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: Riffled Independence for Ranked Data »
Jonathan Huang · Carlos Guestrin -
2009 Spotlight: Riffled Independence for Ranked Data »
Jonathan Huang · Carlos Guestrin -
2009 Spotlight: Submodularity Cuts and Applications »
Yoshinobu Kawahara · Kiyohito Nagano · Koji Tsuda · Jeffrey A Bilmes -
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 -
2007 Oral: Efficient Inference forDistributions on Permutations »
Jonathan Huang · Carlos Guestrin · Leonidas Guibas -
2007 Poster: Efficient Inference forDistributions on Permutations »
Jonathan Huang · Carlos Guestrin · Leonidas Guibas -
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 -
2007 Poster: Efficient Principled Learning of Thin Junction Trees »
Anton Chechetka · Carlos Guestrin -
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 -
2006 Poster: Distributed Inference in Dynamical Systems »
Stanislav Funiak · Carlos Guestrin · Mark A Paskin · Rahul Sukthankar