| AC member | Descriptors |
|
Naoki Abe | Applications to business analytics and optimization;
Web and social network mining; E-commerce;
Semi-supervised learning; Clustering; Outlier detection;
Cost-sensitive learning; Reinforcement learning; Active learning;
Ensemble learning; Online learning; Graphical models |
| Yasemin
Altun | Kernel methods; Structured output prediction;
Graphical models; Semi-supervised learning; Natural language processing |
| Francis Bach | Kernel methods; Sparse methods;
Optimization; Clustering; Semi-supervised learning; Matrix
factorization; Computer vision; Learning on graphs |
| Samy Bengio | Applications of speech and image
processing; Document retrieval; Large-scale learning; Deep architectures |
| David Blei |
Nonparametric Bayesian methods; Topic modeling; Latent variable
modeling; Approximate posterior inference; Applications of ML to text |
| Carla Brodley | Applications of ML, including medicine, SNA, science, engineering; Active learning, cost-sensitive learning, and clustering |
|
Florence d'Alche Buc | Kernel methods; Structured output
prediction; Learning in graphical models; Dynamical systems modeling;
Application to computational biology and systems biology |
| Luc De Raedt | Logical and relational learning;
Relational learning (statistical); Inductive logic programming
(probabilistic); Symbolic and knowledge-based approaches to learning;
Pattern mining and inductive querying; Learning from structured data using symbolic methods |
| Marie desJardins | Learning with background knowledge;
Active learning; Clustering; Preference learning; Relational learning;
Multi-agent learning; Transfer learning |
| Kurt Driessens | Relational reinforcement learning;
Transfer learning; Action and activity learning; Relational learning;
Inductive logic programming; Multi-agent learning |
| Alan
Fern | Reinforcement learning; Relational learning;
Structured prediction; Transfer learning; Learning for planning;
Learning for search |
| David
Forsyth | Computer vision; Object recognition; Computer
animation; Human activity recognition |
| Johannes Fuernkranz | Classification-rule learning;
Decision-tree learning; Preference learning; Evaluation methodology;
ROC analysis; Noise handling; Machine learning in games |
| Kenji
Fukumizu | Kernel methods; Dimensionality reduction; Active
learning; Dependence analysis; Information geometry |
| John Langford |
Learning theory; Interactive learning; Large scale learning;
Exploration; Active learning; Reinforcement learning. See also
blog |
| Mirella
Lapata | Classification and prediction; Data mining;
Evaluation and methodology; Information and document retrieval;
Natural language processing; Structured and relational data; Web and
search |
| Neil
Lawrence | Gaussian processes; Dimensionality reduction;
latent variable models; Probabilistic models; Approximate inference;
Applications in computational biology and human motion |
| Yann LeCun |
Deep learning; Vision; Stochastic optimization; Non-convex
optimization; Energy-based models; Structured output models;
Unsupervised learning; Sparse representations; Models of biological
learning; Neural networks |
| Sofus
Attila Macskassy | Statistical relational
learning; Learning from structured data; Learning on graphs; Pattern
and graph mining; Social network analysis; Dynamic network analysis;
Semi-supervised learning; ROC analysis; Evaluation methods |
| Yishay
Mansour | Computational learning theory; Algorithmic game
theory; Theory of Markov decision processes |
|
Steven Minton | Learning and the web; Learning to extract
information; Learning and planning/scheduling/constraints/problem
solving/search; Learning and information integration; Learning methods
for record linkage |
| Dunja
Mladenic | Learning on text/documents; Classification-rule
learning; Decision-tree learning; Semi-supervised learning; Feature
selection; Personalization and recommendation systems |
| Tim
Oates | Reinforcement learning; Machine learning for
robotics; Natural language processing; Grammar induction; Computer
vision; Grounded language learning |
| Michael Pazzani | Learning and commonsense reasoning; Transfer learning; Personalization and
recommendation systems; Empirical insights into ML; Models of Human Learning |
| Massimiliano
Pontil | Multi-task learning; Transfer learning; Kernel
selection; Multiple kernel learning; Convex optimization; Sparse
estimation; Compressed sensing; Matrix factorization; Prediction on
graphs; Metric Learning; Clustering; Regularization |
| Pascal Poupart | Reinforcement learning; Bayesian
reinforcement learning; Multi-agent reinforcement learning; Inverse
reinforcement learning; Predictive state representation; Markov
decision processes; Partially observable Markov decision processes;
Hidden Markov models; Gaussian processes; Sequential decision making;
Active learning; Preference elicitation |
| Carl Rasmussen |
Bayesian inference; Gaussian processes; Reinforcement learning;
Latent variable models; Approximate inference; Markov chain Monte
Carlo |
| Martin Riedmiller | Reinforcement learning; Machine
learning for robotics; Policy gradient methods; Neurodynamic
programming; Fitted value iteration; Fitted Q iteration; Real life
reinforcement learning |
| Dan Roth |
On-line classification; Ranking; Structure learning; Learning with
constraints; Active learning; Semi-supervised learning; Learning
theory; Natural language processing; Information extraction |
| Volker
Roth | Kernel methods; Bayesian inference; Sparsity and
feature selection; Clustering; Bio-medical applications & image analysis |
| Michele Sebag |
Stochastic optimization; Genetic/evolutionary algorithms;
Relational learning; Meta-learning; Clustering; Data streaming;
Applications of ML: Autonomic Computing; Robotics |
| Fei Sha | Structured prediction; Manifold learning;
Dimensionality reduction; Semi-supervised learning; Optimization;
Latent variable modeling; Speech processing and recognition |
| Yoram
Singer | Large margin methods; Boosting algorithms; Kernel
methods; Structured data; Learning theory |
| Nathan Srebro | Optimization for ML; Multi-task learning; Spectral regularization and
matrix factorization approaches; Clustering; Statistical learning
theory; Kernel methods; Computational tractability in ML |
| Luis Torgo |
Regression; Tree-based models; Prediction of rare values;
Utility-based learning; Outlier detection; Time-series analysis;
Applications of ML/DM to financial markets; Ecology and fraud
detection |
| Yee Whye
Teh | Nonparametric Bayesian models; Latent variable
models; Graphical models; Probabilistic models; Approximate inference; Deep representations |
| David Wingate | Reinforcement
learning; Predictive
representations of state; Manifold learning; Bayesian reinforcement
learning; Visual perception; Dynamical systems modeling; Hierarchical
Bayesian learning |
| Nevin Zhang | Model-based clustering, latent variable models; Learning with probabilistic graphical models |
| Martin Zinkevich | Theory of multi-agents; Mechanism design; Game theory; Online algorithms |
