Tomi Silander - RankMe

49 papers A* 3A 9B 5Misc 1Journal 18Unranked 13

Year	Rank	Type	Title / Venue / Authors
2024	A	`conf`	Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses. ECAI Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel
2024	J	`jnl`	Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses. CoRR Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel
2024	J	`jnl`	Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. CoRR Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos
2023	—	`conf`	A Hierarchical Scheme for Adapting Learned Quadruped Locomotion. Humanoids Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères
2023	J	`jnl`	Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning. CoRR Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères
2022	—	`conf`	DiPCAN: Distilling Privileged Information for Crowd-Aware Navigation. Robotics: Science and Systems Gianluca Monaci, Michel Aractingi, Tomi Silander
2020	J	`jnl`	Learning Synthetic to Real Transfer for Localization and Navigational Tasks. CoRR Maxime Pietrantoni, Boris Chidlovskii, Tomi Silander
2019	J	`jnl`	Optimal Policies for Observing Time Series and Related Restless Bandit Problems. J. Mach. Learn. Res. Christopher R. Dance, Tomi Silander
2018	B	`conf`	Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets. ALT Elias Jääsaari, Janne Leppä-aho, Tomi Silander, Teemu Roos
2018	A	`conf`	Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. AISTATS Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos
2017	—	`conf`	Hyperparameter sensitivity revisited. AMBN Tomi Silander
2017	J	`jnl`	Non-Markovian Control with Gated End-to-End Memory Policy Networks. CoRR Julien Perez, Tomi Silander
2017	J	`jnl`	Scalable transfer learning in heterogeneous, dynamic environments. Artif. Intell. Trung Thanh Nguyen, Tomi Silander, Zhuoru Li, Tze-Yun Leong
2015	—	`conf`	When are Kalman-Filter Restless Bandits Indexable? NIPS Christopher R. Dance, Tomi Silander
2014	B	`conf`	Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury. ICPR Bolan Su, Thien Anh Dinh, Abhinit Kumar Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu, C. C. Tchoyoson Lim, Boon Chuan Pang, Cheng Kiang Lee, Tze-Yun Leong, Chew Lim Tan
2014	A*	`conf`	Bootstrapping Simulation-Based Algorithms with a Suboptimal Policy. ICAPS Truong-Huy Dinh Nguyen, Tomi Silander, Wee Sun Lee, Tze-Yun Leong
2013	B	`conf`	A Dynamic Programming Algorithm for Learning Chain Event Graphs. Discovery Science Tomi Silander, Tze-Yun Leong
2013	B	`conf`	Do risk-averse people lie less? A comparison of risk-taking behavior in deceptive and non-deceptive scenarios. CogSci Tei Laine, Kayo Sakamoto, Tomi Silander
2013	J	`jnl`	Minimum Encoding Approaches for Predictive Modeling CoRR Peter Grünwald, Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
2013	J	`jnl`	On Supervised Selection of Bayesian Networks CoRR Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
2013	—	`conf`	Online Feature Selection for Model-based Reinforcement Learning. ICML (1) Trung Thanh Nguyen, Zhuoru Li, Tomi Silander, Tze-Yun Leong
2013	—	`conf`	Unsupervised Medical Image Classification by Combining Case-Based Classifiers. MedInfo Thien Anh Dinh, Tomi Silander, Bolan Su, Tianxia Gong, Boon Chuan Pang, C. C. Tchoyoson Lim, Cheng Kiang Lee, Chew Lim Tan, Tze-Yun Leong
2012	J	`jnl`	A simple approach for finding the globally optimal Bayesian network structure. CoRR Tomi Silander, Petri Myllymäki
2012	Misc	`conf`	An automated pathological class level annotation system for volumetric brain images. AMIA Thien Anh Dinh, Tomi Silander, C. C. Tchoyoson Lim, Tze-Yun Leong
2012	J	`jnl`	On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter CoRR Tomi Silander, Petri Kontkanen, Petri Myllymäki
2012	—	`conf`	Transferring Expectations in Model-based Reinforcement Learning. NIPS Trung Thanh Nguyen, Tomi Silander, Tze-Yun Leong
2010	J	`jnl`	Learning locally minimax optimal Bayesian networks. Int. J. Approx. Reason. Tomi Silander, Teemu Roos, Petri Myllymäki
2009	A	`conf`	Locally Minimax Optimal Predictive Modeling with Bayesian Networks. AISTATS Tomi Silander, Teemu Roos, Petri Myllymäki
2008	—	`conf`	A Bayesian approach to learning in fault isolation. BMA Hannes Wettig, Anna Pernestål, Tomi Silander, Mattias Nyberg
2007	A	`conf`	On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter. UAI Tomi Silander, Petri Kontkanen, Petri Myllymäki
2006	A	`conf`	A Simple Approach for Finding the Globally Optimal Bayesian Network Structure. UAI Tomi Silander, Petri Myllymäki
2006	J	`jnl`	Analysis of HIV-1 Bioinform. Koen Deforche, Tomi Silander, Ricardo Camacho, Zehava Grossman, M. A. Soares, Kristel Van Laethem, Rami Kantor, Yves Moreau, Anne-Mieke Vandamme
2006	J	`jnl`	Learning probabilistic decision graphs. Int. J. Approx. Reason. Manfred Jaeger, Jens Dalgaard Nielsen, Tomi Silander
2004	—	`conf`	A Scalable Topic-Based Open Source Search Engine. Web Intelligence Wray L. Buntine, Jaakko Löfström, Jukka Perkiö, Sami Perttu, Vladimir Poroshin, Tomi Silander, Henry Tirri, Antti J. Tuominen, Ville H. Tuulos
2002	J	`jnl`	B-Course: A Web-Based Tool for Bayesian and Causal Data Analysis. Int. J. Artif. Intell. Tools Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen
2001	B	`conf`	B-Course: A Web Service for Bayesian Data Analysis. ICTAI Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen
2001	A*	`conf`	Bayesian Data Mining on the Web with B-Course. ICDM Petri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen
2000	J	`jnl`	On predictive distributions and Bayesian networks. Stat. Comput. Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald
2000	J	`jnl`	Supervised model-based visualization of high-dimensional data. Intell. Data Anal. Petri Kontkanen, Jussi Lahtinen, Petri Myllymäki, Tomi Silander, Henry Tirri
1999	A	`conf`	Exploring the robustness of Bayesian and information-theoretic methods for predictive inference. AISTATS Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1999	A	`conf`	On Supervised Selection of Bayesian Networks. UAI Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1998	A*	`conf`	BAYDA: Software for Bayesian Classification and Feature Selection. KDD Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1998	—	`conf`	Batch Classification with Discrete Finite Mixtures. ECML Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1998	—	`conf`	Bayes Optimal Instance-Based Learning. ECML Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1998	—	`conf`	Bayesian and Information-Theories Priors for Bayesian Network Parameters. ECML Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald
1998	A	`conf`	Minimum Encoding Approaches for Predictive Modeling. UAI Peter Grünwald, Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1998	—	`conf`	On Bayesian Case Matching. EWCBR Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri
1997	A	`conf`	Comparing Predictive Inference Methods for Discrete Domains. AISTATS Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald
1990	J	`jnl`	Orthogonal type extensions and reductions. ACM SIGPLAN Notices Jukka Paakki, Anssi Karhinen, Tomi Silander