Liu Yang liuy@cs.cmu.edu     My CV 7223 GHC, Computer Science Department, Carnegie Mellon University Pittsburgh, PA 15213-3891 Tel: (517) 526-2509 (cell)     (412) 268-7669 (office) I recently completed my Ph.D. with Avrim Blum and Jaime Carbonell at Carnegie Mellon University. I'm now a postdoctoral fellow in the Computer Science Department. at CMU. My area of research is Theoretical Machine Learning and Theoritical Computer Science. I'm particularly interested in Statistical and Computational Learning Theory, Property Testing, Algorithmic Economics.

New Directions in Machine Learning: Research Statement Teaching Statement Topics I've been working on: Learning with Drifting Concepts and Distributions Sublinear Algorithms: Active Property Testing, Active Tolerant Testing Mechanism Design: Online Allocation/Pricing Problems with Economies of Scale, Correlated Auctions, Repeated Auctions, Prior Estimation applied to Combinatorial Auctions Learning DNF with Representation-Specific Queries Theory of Transfer Learning/Online Learning/Active Learning (Bayesian Active Learning, Minimax Analysis of Active Learning, Active Learning with Convex Losses, Buy-in-Bulk Active Learning) Learning Distance Metrics (DistLearnKit), with applicatoins to Computer Vision, Interactive Search-Assisted Diagnosis (CMU Diamond and Intel Research)

Publications

Preprints and Working Papers

• Learning with a Drifting Target Concept. With Steve Hanneke and Varun Kanade. In submission to the Journal of Machine Learning Research.
• Minimax Analysis of Active Learning. With Steve Hanneke. In submission to the Journal of Machine Learning Research.
• Estimation of Priors with Applications to Preference Elicitation. With Steve Hanneke. In submission to the 28th AAAI Conference on Artificial Intelligence.
• Online Allocation and Pricing with Economies of Scale. With Avrim Blum and Yishay Mansour. In submission to The 46th Annual ACM Symposium on Theory of Computing (STOC), 2014.
• Surrogate Losses in Passive and Active Learning. With Steve Hanneke. [arXiv:1207.3772]. In submission to the Annals of Statistics.
• Thesis Defense: Mathematical Theories of Interaction with Oracles. Ph.D. Thesis, 2013. Thesis Committee: Avrim Blum, Manuel Blum, Jaime Carbonell, Sanjoy Dasgupta, Yishay Mansour, Joel Spencer. Slides.

2013

• Buy-in-Bulk Active Learning. Liu Yang and Jaime Carbonell. Advances in Neural Information Processing Systems 26 (NIPS), 2013.
• Liu Yang, Avrim Blum and Jaime Carbonell. Learnability of DNF with Representation-Specific Queries. Innovations in Theoretical Computer Science (ITCS), 2013. Slides.
  
• Liu Yang and Steve Hanneke. Activized Learning with Uniform Classification Noise. The International Conference on Machine Learning (ICML), 2013.

2012

• Active Property Testing. With Nina Balcan, Eric Blais, Avrim Blum. FOCS 2012. [arXiv:1111.0897v2 (full version)].
• Liu Yang, Steve Hanneke and Jaime Carbonell. A Theory of Transfer Learning with Applications to Active Learning. Machine Learning, 2012.
  
• Liu Yang and Jaime Carbonell. Buy-in-Bulk Active Learning. Tech report CMU-ML-12-110.
• Liu Yang, Steve Hanneke and Jaime Carbonell. Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. Tech report CMU-ML-12-112.

2011

• Thesis Proposal: Active Perspectives on Computational Learning and Testing. Slides.
  
• Liu Yang. Active Learning with a Drifting Distribution. Advances in Neural Information Processing Systems (NIPS) 2011.
• Liu Yang, Steve Hanneke and Jaime Carbonell. Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning . The 24th Annual Conference on Learning Theory (COLT), Budapest, Hungary, 2011.
• Liu Yang, Steve Hanneke and Jaime Carbonell. The Sample Complexity of Self-Verifying Bayesian Active Learning. International Conference on Artificial Intelligence and Statisitcs (AISTATS), 2011.

2010

• Liu Yang, Steve Hanneke and Jaime Carbonell. Characterizing Optimal Rates for Lossy Coding with Finite-Dimensional Metrics. Manuscript.
• Liu Yang, Steve Hanneke and Jaime Carbonell. Bayesian Active Learning Using Arbitrary Binary Valued Queries. Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT), 2010.
• Steve Hanneke and Liu Yang. Negative Results for Active Learning with Convex Losses. International Conference on Artificial Intelligence and Statisitcs (AISTATS), 2010.

2009

• Liu Yang and Jaime Carbonell. Cost Complexity of Proactive Learning via a Reduction to Realizable Active Learning. Tech report CMU-ML-09-113.
• Liu Yang and Jaime Carbonell. Adaptive Proactive Learning with Cost-Reliability Tradeoff. Tech report CMU-ML-09-114.
• Liu Yang, Rong Jin and Jieping Ye. Online Learning by Ellipsoid Method. The International Conference on Machine Learning (ICML), 2009.
• Liu Yang, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Stephen Hoi and Madadev Satyanarayanan. A Boosting Framework for Visuality-Preserving Distance Metric Learning and its Application to Medical Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 January, 32(1):30-44..

2008

• Liu Yang, Rong Jin and Rahul Sukthankar. Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization. Advances in Neural Information Processing Systems (NIPS) 2008.
• Liu Yang, Rong Jin, Rahul Sukthankar and Frederic Jurie. Unifying Discriminative Visual Codebook Generation with Classifier Training for Object Category Recognition.Proceedings of Computer Vision and Pattern Recognition, 2008.(Oral Presentation)

2007

• Liu Yang, Rong Jin and Rahul Sukthankar. Bayesian Active Distance Metric Learning. Proceedings of UAI, 2007. (Oral Presentation)
• Liu Yang, Rong Jin, Caroline Pantofaru and Rahul Sukthankar. Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data. Proceedings of Computer Vision and Pattern Recognition, 2007.
• Liu Yang, Rong Jin and Rahul Sukthankar. Resource-Constrained Supervised Dimensionality Reduction. Proceedings of IJCAI Workshop on Multimodal Information Retrieval, 2007.
• Liu Yang, Rong Jin and Rahul Sukthankar, Bin Zheng, Lily Mummert, Mahadev Satyanarayanan, Mei Chen and Drazen Jukic. Learning Distance Metrics for Interactive Search-Assisted Diagnosis of Mammograms. Proceedings of SPIE Medical Imaging (Oral Presentation), 2007.

2006

• Liu Yang, Rong Jin, Rahul Sukthankar and Yi Liu. An Efficient Algorithm for Local Distance Metric Learning. (Oral Prensentation) Proceedings of AAAI, 2006.
• Yi Liu, Rong Jin and Liu Yang. Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization. (Oral Prensentation) Proceedings of AAAI, 2006.

Services

Program Committee: ICML (2012, 2013). Reviewer: SODA (2013), ICML (2009), COLT 2011, CVPR (2012, 2013), ICCV 2013, J. Machine Learning Research, IEEE Trans Pattern Anal Mach Intell. (TPAMI).

Lectures and Serminar Talks

• Invited lecture on Online Pricing, Correlated Auction in 15-896 Algorithms, Games, and Networks (Spring 2013), taught by Avrim Blum and Ariel Procaccia in the Computer Science Dept., CMU.
• Invited lectures on Property Testing, Theory of Active Learning, Transfer Learning in 15-859(B) Machine Learning Theory (Spring 2012), taught by Avrim Blum in the Computer Science Dept., CMU.
• Teaching assistant for 15-355 Modern Computer Algebra (Fall 2011), taught by Victor Adamchik in the Computer Science Dept., CMU. Invited guest lecture on Gröbner Bases.
  
• Teaching assistant for 15-750 Graduate Algorithms (Spring 2011), taught by Manuel Blum in the Computer Science Dept., CMU. Invited guest lecture on Online Learning Mistake Bound Model.


• Online Allocation and Pricing with Economies of Scale, the ACO Seminar, Department of Mathematical Sciences, Carnegie Mellon University, Nov 14, 2013.
• Active Testing Real-valued Functions, CMU Theory Lunch, Algorithms and Complexity Theory Group, Carnegie Mellon University, Oct 30, 2013.
• Active Testing Boolean and Real-valued Functions, CS Theory/Math Seminar, Purdue University, Oct 21, 2013.
• Online Allocation and Pricing with Economies of Scale, Economics and Computer Science Research Seminar, EconCS Group, School of Engineering and Applied Science, Harvard University, Sep 13, 2013.
• Invited lecture on A Theory of Transfer Learning with Applications to Active Learning, at the Dept. of Brain and Cognitive Sciences, MIT, hosted by Tomaso Poggio, Feb 27, 2013.
• Invited lecture on Combinatorial Approaches to Active Learning and Transfer Learning, the Courant Institute of Mathematical Sciences, Feb 19, 2013.
• Invited lecture on Mathematical Theories of Interaction with Oracles, School of Mathematics, Institute for Advanced Study, Feb 11, 2013.
• Active Learning, Drifting Distributions, and Convex Losses, CMU Machine Learning Lunch Seminar, Apr 30, 2012. Slides. Invited talk on this at EECS Dept., UC Berkeley, hosted by Peter Bartlett, Jan 23, 2013.
• Active Property Testing, Algorithms and Complexity Seminar, MIT CSAIL Theory of Computation, MIT, Oct 4, 2012. Slides.
• Active Property Testing, Berkeley Theory Seminar, EECS Dept., UC Berkeley, Sep 24, 2012. More recent talk at CS Theory Group, IBM Almaden Research Center, Jan 14, 2013. Slides.
• Learnability of DNF with Representation-Specific Queries. CMU Theory Lunch, Algorithms and Complexity Theory Group, Carnegie Mellon University, Apr 11, 2012.
• Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning, CMU Machine Learning Lunch Seminar, Oct 31, 2011. Slides.

Selected Past Talks

• Online Learning by Ellipsoid Method. June 15, 2009. Oral Presentation in the 26th International Conference on Machine Learning, Montreal, Canada.
• Bayesian Active Distance Metric Learning. July 20, 2007. Oral Presentation in the 23rd Conference on Uncertainty in Artificial Intelligence, University of British Columbia Vancouver, BC, Canada.
• Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data. July 19, 2007. Vision and Media Lab, Simon Fraser University, Burnaby, BC, Canada.
• Improving Object Recognition by Discriminative Cluster Refinement. Vision and Autonomous Systems Center (VASC) Seminar, The Robotics Institute, Carnegie Mellon University, June 11, 2007.
• Local Distance Metric Learning. July 21, 2006. Biomedical Imaging and Analysis Seminar, Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, Cambridge.
• Applying Local Distance Metric Learning to Image Retrieval. July 21, 2006. Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
• An Efficient Algorithm for Local Distance Metric Learning. July 18, 2006. AAAI-06: Twenty-First National Conference on Artificial Intelligence, Boston, Massachusetts.

Distance Metric Learning: A Comprehensive Survey [slides]
An Overview of Distance Metric Learning

[Last updated 2014]