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Tomi Silander
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2020 – today
- 2024
- [c31]Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel:
Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses. ECAI 2024: 4393-4401 - [i9]Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel:
Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses. CoRR abs/2408.14527 (2024) - [i8]Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos:
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. CoRR abs/2408.14935 (2024) - 2023
- [c30]Michel Aractingi, Pierre-Alexandre Léziart
, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères:
A Hierarchical Scheme for Adapting Learned Quadruped Locomotion. Humanoids 2023: 1-8 - [i7]Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères:
Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning. CoRR abs/2309.16683 (2023) - 2022
- [c29]Gianluca Monaci
DiPCAN: Distilling Privileged Information for Crowd-Aware Navigation. Robotics: Science and Systems 2022 - 2020
- [i6]Maxime Pietrantoni, Boris Chidlovskii, Tomi Silander:
Learning Synthetic to Real Transfer for Localization and Navigational Tasks. CoRR abs/2011.10274 (2020)
2010 – 2019
- 2019
- [j9]Christopher R. Dance, Tomi Silander:
Optimal Policies for Observing Time Series and Related Restless Bandit Problems. J. Mach. Learn. Res. 20: 35:1-35:93 (2019) - 2018
- [c28]Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos:
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. AISTATS 2018: 948-957 - [c27]Elias Jääsaari, Janne Leppä-aho, Tomi Silander, Teemu Roos:
Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets. ALT 2018: 470-488 - 2017
- [j8]Trung Thanh Nguyen, Tomi Silander, Zhuoru Li, Tze-Yun Leong
Scalable transfer learning in heterogeneous, dynamic environments. Artif. Intell. 247: 70-94 (2017) - [c26]Tomi Silander:
Hyperparameter sensitivity revisited. AMBN 2017: 7 - [i5]Julien Perez, Tomi Silander:
Non-Markovian Control with Gated End-to-End Memory Policy Networks. CoRR abs/1705.10993 (2017) - 2015
- [c25]Christopher R. Dance, Tomi Silander:
When are Kalman-Filter Restless Bandits Indexable? NIPS 2015: 1711-1719 - 2014
- [c24]Truong-Huy Dinh Nguyen, Tomi Silander, Wee Sun Lee, Tze-Yun Leong:
Bootstrapping Simulation-Based Algorithms with a Suboptimal Policy. ICAPS 2014 - [c23]Bolan Su, Thien Anh Dinh, Abhinit Kumar Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu
Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury. ICPR 2014: 3245-3250 - 2013
- [c22]Tei Laine, Kayo Sakamoto, Tomi Silander:
Do risk-averse people lie less? A comparison of risk-taking behavior in deceptive and non-deceptive scenarios. CogSci 2013 - [c21]Tomi Silander, Tze-Yun Leong
A Dynamic Programming Algorithm for Learning Chain Event Graphs. Discovery Science 2013: 201-216 - [c20]Trung Thanh Nguyen, Zhuoru Li, Tomi Silander, Tze-Yun Leong:
Online Feature Selection for Model-based Reinforcement Learning. ICML (1) 2013: 498-506 - [c19]Thien Anh Dinh, Tomi Silander, Bolan Su, Tianxia Gong, Boon Chuan Pang, C. C. Tchoyoson Lim
Unsupervised Medical Image Classification by Combining Case-Based Classifiers. MedInfo 2013: 739-743 - [i4]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
On Supervised Selection of Bayesian Networks. CoRR abs/1301.6710 (2013) - [i3]Peter Grünwald, Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Minimum Encoding Approaches for Predictive Modeling. CoRR abs/1301.7378 (2013) - 2012
- [c18]Thien Anh Dinh, Tomi Silander, C. C. Tchoyoson Lim, Tze-Yun Leong:
An automated pathological class level annotation system for volumetric brain images. AMIA 2012 - [c17]Trung Thanh Nguyen, Tomi Silander, Tze-Yun Leong:
Transferring Expectations in Model-based Reinforcement Learning. NIPS 2012: 2564-2572 - [i2]Tomi Silander, Petri Kontkanen, Petri Myllymäki:
On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter. CoRR abs/1206.5293 (2012) - [i1]Tomi Silander, Petri Myllymäki:
A simple approach for finding the globally optimal Bayesian network structure. CoRR abs/1206.6875 (2012) - 2010
- [j7]Tomi Silander, Teemu Roos
Learning locally minimax optimal Bayesian networks. Int. J. Approx. Reason. 51(5): 544-557 (2010)
2000 – 2009
- 2009
- [c16]Tomi Silander, Teemu Roos, Petri Myllymäki:
Locally Minimax Optimal Predictive Modeling with Bayesian Networks. AISTATS 2009: 504-511 - 2008
- [c15]Hannes Wettig, Anna Pernestål, Tomi Silander, Mattias Nyberg:
A Bayesian approach to learning in fault isolation. BMA 2008 - 2007
- [c14]Tomi Silander, Petri Kontkanen, Petri Myllymäki:
On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter. UAI 2007: 360-367 - 2006
- [j6]Koen Deforche, Tomi Silander, Ricardo Camacho
Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance. Bioinform. 22(24): 2975-2979 (2006) - [j5]Manfred Jaeger, Jens Dalgaard Nielsen, Tomi Silander:
Learning probabilistic decision graphs. Int. J. Approx. Reason. 42(1-2): 84-100 (2006) - [c13]Tomi Silander, Petri Myllymäki:
A Simple Approach for Finding the Globally Optimal Bayesian Network Structure. UAI 2006 - 2004
- [c12]Wray L. Buntine
A Scalable Topic-Based Open Source Search Engine. Web Intelligence 2004: 228-234 - 2002
- [j4]Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen:
B-Course: A Web-Based Tool for Bayesian and Causal Data Analysis. Int. J. Artif. Intell. Tools 11(3): 369-387 (2002) - 2001
- [c11]Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen:
Bayesian Data Mining on the Web with B-Course. ICDM 2001: 626-629 - [c10]Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen:
B-Course: A Web Service for Bayesian Data Analysis. ICTAI 2001: 247-256 - 2000
- [j3]Petri Kontkanen, Jussi Lahtinen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Supervised model-based visualization of high-dimensional data. Intell. Data Anal. 4(3-4): 213-227 (2000) - [j2]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald:
On predictive distributions and Bayesian networks. Stat. Comput. 10(1): 39-54 (2000)
1990 – 1999
- 1999
- [c9]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Exploring the robustness of Bayesian and information-theoretic methods for predictive inference. AISTATS 1999 - [c8]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
On Supervised Selection of Bayesian Networks. UAI 1999: 334-342 - 1998
- [c7]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Bayes Optimal Instance-Based Learning. ECML 1998: 77-88 - [c6]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald:
Bayesian and Information-Theories Priors for Bayesian Network Parameters. ECML 1998: 89-94 - [c5]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Batch Classification with Discrete Finite Mixtures. ECML 1998: 208-213 - [c4]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
On Bayesian Case Matching. EWCBR 1998: 13-24 - [c3]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
BAYDA: Software for Bayesian Classification and Feature Selection. KDD 1998: 254-258 - [c2]Peter Grünwald, Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri:
Minimum Encoding Approaches for Predictive Modeling. UAI 1998: 183-192 - 1997
- [c1]Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald:
Comparing Predictive Inference Methods for Discrete Domains. AISTATS 1997: 311-318 - 1990
- [j1]Jukka Paakki, Anssi Karhinen, Tomi Silander:
Orthogonal type extensions and reductions. ACM SIGPLAN Notices 25(7): 28-38 (1990)
Coauthor Index
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last updated on 2024-10-28 21:16 CET by the dblp team
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