CFP: Cognitive Models of Learning at ICML 2010.
We invite you to submit your work on cognitive models of learning to the 27th International Conference on Machine Learning (ICML-10). ICML is a premier machine learning conference similar to NIPS. Like NIPS, papers published at ICML receive great attention from the learning community. Unlike NIPS, however, the area of cognitive models in ICML has not been as well-publicized in recent years.
Our goal is to have a much broader scope and a better coverage of all subfields to learning.
Specifically, we welcome submissions on all *computational* models of learning in cognitive science. Deadline: Feb. 1, 2010.
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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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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North American Computational Linguistics Olympiad (NACLO 2009) Wisconsin site 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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