Research Interests:
machine learning (semi-supervised learning, latent variable models and topic models, Bayesian methods), and its applications in natural language processing,
cognitive psychology,
and human computer interaction.
Full publication list
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Recent Highlights:
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Semi-Supervised Learning
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We published a book for beginners:
Introduction to semi-supervised learning. Morgan & Claypool, 2009. [available online]
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A concise, more technical summary of semi-supervised learning, in Encyclopedia of Machine Learning, to appear [pdf]
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(AISTATS 09) Multi-manifold semi-supervised learning -- learning when data lives on multiple, intersecting manifolds. [pdf]
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(NIPS 08) Unlabeled data: now it helps, now it doesn't -- a finite-sample minimax analysis on when semi-supervised learning is better than supervised learning. [pdf]
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(ECML 08) Online semi-supervised learning -- learning when labeled and unlable items arrive sequentially. [pdf]
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Semi-supervised learning tutorials [Chicago Summer School 2009,
ICML 2007]
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Semi-supervised learning literature survey [link]
- Latent Topic Models:
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(ICML 09) Dirichlet forest priors -- shaping latent topic models with domain knowledge, coded as a mixture of Dirichlet trees [pdf]
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(ECML 07) Statistical debugging using latent topic models [pdf]
- Computational Cognitive Psychology
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(NIPS 09) Human Rademacher complexity -- bounding human generalization ability using computational learning theory [pdf]
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(NIPS 08) Human active learning -- human learning rate is exponential (similar to active machine learning rate), if they can select queries [pdf]
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(AAAI 08) Monkey online learning -- modeling concept-drift in Wisconsin Card Sort Task [pdf]
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(AAAI 07) Human semi-supervised learning -- humans can learn from labeled and unlabeled data [pdf]
- Natural Language Processing
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(NAACL 09) May all your wishes come true: A study of wishes and how to recognize them -- a new type of sentiment analysis [pdf]
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(ACL 08) Learning bigrams from unigrams -- seemingly impossible, but the information is hidden in marginal distributions [pdf]
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(AAAI 07) Text-to-Picture synthesis -- turning English sentences into pictures [pdf]
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(NAACL 07) Improving diversity in ranking using absorbing random walks -- ranking items a la PageRank while maximizing diversity [pdf]
Current Professional Activities
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Editorial Board, Machine Learning Journal
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Program Committee / Referee:
ICML 2010 (Area Chair),
AAAI 2010,
AISTATS 2010,
COLING 2010,
NIPS 2009,
UAI 2009,
ICML 2009,
ACL 2009,
NAACL 2009,
AISTATS 2009
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North American Computational Linguistics Olympiad (NACLO 2010) University of Wisconsin-Madison site 2010
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AAAI 2009 Fall Symposium on Manifold Learning and its Applications
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University of Chicago Summer School/Workshop on Theory and Practice of Computational Learning, 2009
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NIPS 2008 Workshop on Machine Learning Meets Human Learning
Ph.D. Carnegie Mellon University, 2005. (C.V.)
Teaching
Machine Learning Group Members:
David Andrzejewski,
Bryan Gibson,
Andrew Goldberg,
Kwang-Sung Jun,
Junming Sui
Alumni
Lijie Heng, MS 2008, Oracle
Jurgen Van Gael, MS 2007, Ph.D. student at University of Cambridge
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