T. Silander

发表

Bayesian network structure learning using factorized NML universal models

T. Roos, T. Silander, P. Kontkanen, 2008, 2008 Information Theory and Applications Workshop.

A Comparison of Non-informative Priors for Bayesian Networks

T. Silander, H. Tirri, 1998 .

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors.

T. Silander, D. Katzenstein, Y. Moreau, 2007, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

A Simple Approach for Finding the Globally Optimal Bayesian Network Structure pdf

Tomi Silander, Petri Myllymäki, T. Silander, 2006, UAI.

Transfer Learning as Representation Selection

T. Silander, T. Leong, T. Nguyen-Thanh, 2012 .

Improving the Generalization of Visual Navigation Policies using Invariance Regularization

T. Silander, C. Dance, Michel Aractingi, 2019 .

USING BAYESIAN NETWORKS FORVISUALIZING HIGH-DIMENSIONAL

T. Silander, Petri Myllym, Jussi Lahtinen, 1999 .

Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies

T. Silander, C. Haley, D. de Koning, 2011, BMC proceedings.

Integrating Symbolic Reasoning withNeurally Represented BackgroundKnowledgePetri

T. Silander, P. Myllymaki, P. Orponen, 1992 .

Recent Breakthroughs in Minimum Description Length Learning Workshop

John T. Freitas, T. Roos, T. Silander, 2008 .

9. on the Accuracy of Stochastic Complexity Approximations 9.1 Introduction

T. Silander, 1999 .

A comparison of non-informativepriors for Bayesian networksPetri

T. Silander, 1998 .

An Empirical Evaluation of Stochastic Search Methods in Real-World Telecommunication Domains

T. Silander, H. Wettig, P. Myllymäki, 1999 .

Feature Selection for Model-based Reinforcement Learning

T. Silander, T. Leong, Trung-Thanh Nguyen, 2013 .

DiPCAN: Distilling Privileged Information for Crowd-Aware Navigation

T. Silander, G. Monaci, Michel Aractingi, 2022, Robotics: Science and Systems.

Institutional Knowledge at Singapore Management University

Abhinit Kumar Ambastha, Thien Anh Dinh, T. Silander, 2022 .

Controlling the Solo12 quadruped robot with deep reinforcement learning

T. Silander, P. Souéres, T. Flayols, 2023, Scientific reports.

Non-Markovian Control with Gated End-to-End Memory Policy Networks pdf

Tomi Silander, Julien Perez, T. Silander, 2017, ArXiv.

Is it lie aversion , risk-aversion or tax audit aversion ? Modeling deception under risk and no risk

T. Silander, Kayo Sakamoto, 2015 .

Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data.

Henry Tirri, Tomi Silander, T. Silander, 1998 .

Online Feature Selection for Model-based Reinforcement Learning

Tze-Yun Leong, Tomi Silander, Trung Thanh Nguyen, 2013, ICML.

A Bayesian approach to learning in fault isolation

Tomi Silander, Hannes Wettig, Mattias Nyberg, 2008, BMA.

A Scalable Topic-Based Open Source Search Engine

Henry Tirri, Tomi Silander, Wray L. Buntine, 2004, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04).

Unsupervised Medical Image Classification by Combining Case-Based Classifiers

Tze-Yun Leong, Chew Lim Tan, Tomi Silander, 2013, MedInfo.

Scalable transfer learning in heterogeneous, dynamic environments

Tze-Yun Leong, Tomi Silander, Trung Thanh Nguyen, 2017, Artif. Intell..

Transferring Expectations in Model-based Reinforcement Learning

Tze-Yun Leong, Tomi Silander, Trung Thanh Nguyen, 2012, NIPS.

The Most Probable Bayesian Network and Beyond

Tomi Silander, T. Silander, 2009 .

Optimizing and profiling users online with Bayesian probabilistic modeling

Petri Nokelainen, Henry Tirri, Miikka Miettinen, 2002 .

Investigating the Number of Non-linear and Multi-modal Relationships Between Observed Variables Measuring Growth-oriented Atmosphere

Petri Nokelainen, Henry Tirri, Tomi Silander, 2007 .

A Bayesian Approach for Retrieving Relevant

Tomi Silander, T. Silander, Kontkanen, 1997 .

Orthogonal type extensions and reductions

Anssi Karhinen, Tomi Silander, Jukka Paakki, 1990, SIGP.

On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter

Tomi Silander, Petri Myllymäki, Petri Kontkanen, 2007, UAI.

An automated pathological class level annotation system for volumetric brain images

Tze-Yun Leong, Tomi Silander, C. C. Tchoyoson Lim, 2012, AMIA.

Locally Minimax Optimal Predictive Modeling with Bayesian Networks

Tomi Silander, Teemu Roos, Petri Myllymäki, 2009, AISTATS.

On Supervised Selection of Bayesian Networks pdf

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999, UAI.

Factorized normalized maximum likelihood criterion for learning Bayesian network structures

Tomi Silander, Teemu Roos, Petri Myllymäki, 2008 .

Bayesian network Fisher kernel for categorical feature spaces

Tomi Silander, Teemu Roos, Janne Leppä-aho, 2020, Behaviormetrika.

Bayesian network Fisher kernel for categorical feature spaces

T. Roos, T. Silander, Janne Leppä-aho, 2020, Behaviormetrika.

On predictive distributions and Bayesian networks

Henry Tirri, Tomi Silander, Petri Myllymäki, 2000, Stat. Comput..

Modeling Students' Views on the Advantages of Web-Based Learning with Bayesian Networks

Petri Nokelainen, Henry Tirri, Tomi Silander, 2001 .

B-Course: A Web-Based Tool for Bayesian and Causal Data Analysis

Henry Tirri, Tomi Silander, Petri Myllymäki, 2002, Int. J. Artif. Intell. Tools.

What distinguishes people who turn into tax evaders when properly incentivized from those who don’t? An experimental study using hypothetical scenarios

T. Silander, Kayo Sakamoto, T. Laine, 2020 .

Optimal Policies for Observing Time Series and Related Restless Bandit Problems

Tomi Silander, Christopher R. Dance, T. Silander, 2017, J. Mach. Learn. Res..

A generative model based approach to retrieving ischemic stroke images.

Tze-Yun Leong, Tomi Silander, Thien Anh Dinh, 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

Learning locally minimax optimal Bayesian networks

Tomi Silander, Teemu Roos, Petri Myllymäki, 2010, Int. J. Approx. Reason..

Adversarial Networks for Machine Reading

Tomi Silander, Julien Perez, Quentin Grail, 2018, TAL.

BAYDA: Software for Bayesian Classification and Feature Selection

Henry Tirri, Tomi Silander, Petri Myllymäki, 1998, KDD.

Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury

Shijian Lu, Tze-Yun Leong, Chew Lim Tan, 2014, 2014 22nd International Conference on Pattern Recognition.

Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets

Tomi Silander, Teemu Roos, Janne Leppä-aho, 2018, ALT.

A Dynamic Programming Algorithm for Learning Chain Event Graphs

Tze-Yun Leong, Tomi Silander, T. Silander, 2013, Discovery Science.

Supervised model-based visualization of high-dimensional data

Henry Tirri, Tomi Silander, Petri Myllymäki, 2000, Intell. Data Anal..

A Hierarchical Scheme for Adapting Learned Quadruped Locomotion

T. Silander, P. Souéres, T. Flayols, 2023, 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids).

On the Accuracy of Stochastic Complexity Approximations

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999 .

Exploring the robustness of Bayesian and information-theoretic methods for predictive inference

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999, AISTATS.

Unsupervised Medical Image Classification by Combining Case-Based Classifiers

Thien Anh Dinh, T. Silander, C. Tan, 2013, MedInfo.

Bayes Optimal Instance-Based Learning

Henry Tirri, Tomi Silander, Petri Myllymäki, 1998, ECML.

Bayesian data mining on the Web with B-Course

Henry Tirri, Tomi Silander, Petri Myllymäki, 2001, Proceedings 2001 IEEE International Conference on Data Mining.

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander, Teemu Roos, Janne Leppä-aho, 2018, AISTATS.

When are Kalman-Filter Restless Bandits Indexable?

Tomi Silander, Christopher R. Dance, T. Silander, 2015, NIPS.