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ORCID17th PKDD / 24th ECML 2013: Prague, Czech Republic
- Hendrik Blockeel
, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I. Lecture Notes in Computer Science 8188, Springer 2013, ISBN 978-3-642-40987-5
Reinforcement Learning
- Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin:
A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning. 1-16 - Bilal Piot, Matthieu Geist, Olivier Pietquin:
Learning from Demonstrations: Is It Worth Estimating a Reward Function? 17-32 - Qifeng Qiao, Peter A. Beling:
Recognition of Agents Based on Observation of Their Sequential Behavior. 33-48 - Bastian Bischoff, Duy Nguyen-Tuong, Torsten Koller, Heiner Markert, Alois C. Knoll:
Learning Throttle Valve Control Using Policy Search. 49-64 - Daniel Urieli, Peter Stone:
Model-Selection for Non-parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System. 65-80 - Jan Hendrik Metzen:
Learning Graph-Based Representations for Continuous Reinforcement Learning Domains. 81-96 - Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill:
Regret Bounds for Reinforcement Learning with Policy Advice. 97-112 - Robert William Wright, Steven Loscalzo, Philip Dexter, Lei Yu:
Exploiting Multi-step Sample Trajectories for Approximate Value Iteration. 113-128
Markov Decision Processes
- Joni Pajarinen, Jaakko Peltonen:
Expectation Maximization for Average Reward Decentralized POMDPs. 129-144 - Caroline Ponzoni Carvalho Chanel, Florent Teichteil-Königsbuch:
Properly Acting under Partial Observability with Action Feasibility Constraints. 145-161 - Omar Zia Khan, Pascal Poupart, John Mark Agosta:
Iterative Model Refinement of Recommender MDPs Based on Expert Feedback. 162-177 - Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern:
Solving Relational MDPs with Exogenous Events and Additive Rewards. 178-193 - David Auger, Adrien Couëtoux, Olivier Teytaud:
Continuous Upper Confidence Trees with Polynomial Exploration - Consistency. 194-209
Active Learning and Optimization
- Ali Jalali, Javad Azimi, Xiaoli Z. Fern, Ruofei Zhang:
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization. 210-224 - Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis:
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. 225-240 - Philip Bachman, Doina Precup:
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization. 241-256 - José Bento, Stratis Ioannidis, S. Muthukrishnan, Jinyun Yan:
A Time and Space Efficient Algorithm for Contextual Linear Bandits. 257-272 - Meng Fang, Jie Yin, Xingquan Zhu:
Knowledge Transfer for Multi-labeler Active Learning. 273-288
Learning from Sequences
- Adrià Recasens, Ariadna Quattoni:
Spectral Learning of Sequence Taggers over Continuous Sequences. 289-304 - Jaakko Luttinen:
Fast Variational Bayesian Linear State-Space Model. 305-320 - Ralf Eggeling, André Gohr, Pierre-Yves Bourguignon, Edgar Wingender, Ivo Grosse:
Inhomogeneous Parsimonious Markov Models. 321-336 - Andreas Henelius, Jussi Korpela, Kai Puolamäki:
Explaining Interval Sequences by Randomization. 337-352 - Cheng Zhou, Boris Cule, Bart Goethals:
Itemset Based Sequence Classification. 353-368 - Henrik Grosskreutz, Bastian Lang, Daniel Trabold:
A Relevance Criterion for Sequential Patterns. 369-384 - Jefrey Lijffijt:
A Fast and Simple Method for Mining Subsequences with Surprising Event Counts. 385-400 - Sam Blasiak, Huzefa Rangwala, Kathryn B. Laskey:
Relevant Subsequence Detection with Sparse Dictionary Learning. 401-416
Time Series and Spatio-temporal Data
- Disheng Qiu, Paolo Papotti, Lorenzo Blanco:
Future Locations Prediction with Uncertain Data. 417-432 - Hongyu Guo:
Modeling Short-Term Energy Load with Continuous Conditional Random Fields. 433-448
Data Streams
- Indre Zliobaite, Jaakko Hollmén:
Fault Tolerant Regression for Sensor Data. 449-464 - Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes:
Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. 465-479 - Ezilda Almeida, Carlos Abreu Ferreira, João Gama:
Adaptive Model Rules from Data Streams. 480-492 - Reza Akbarinia, Florent Masseglia:
Fast and Exact Mining of Probabilistic Data Streams. 493-508
Graphs and Networks
- Jan Ramon, Pauli Miettinen, Jilles Vreeken:
Detecting Bicliques in GF[q]. 509-524 - Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, Ambuj K. Singh:
As Strong as the Weakest Link: Mining Diverse Cliques in Weighted Graphs. 525-540 - Abhijin Adiga, Anil Kumar S. Vullikanti:
How Robust Is the Core of a Network? 541-556 - Manish Gupta, Jing Gao, Jiawei Han:
Community Distribution Outlier Detection in Heterogeneous Information Networks. 557-573 - Guo-Xian Yu, Carlotta Domeniconi, Huzefa Rangwala, Guoji Zhang:
Protein Function Prediction Using Dependence Maximization. 574-589 - Cristina Pérez-Solà, Jordi Herrera-Joancomartí:
Improving Relational Classification Using Link Prediction Techniques. 590-605 - Gerben Klaas Dirk de Vries:
A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data. 606-621 - Tamás Horváth, Keisuke Otaki, Jan Ramon:
Efficient Frequent Connected Induced Subgraph Mining in Graphs of Bounded Tree-Width. 622-637 - Elena Valari, Apostolos N. Papadopoulos:
Continuous Similarity Computation over Streaming Graphs. 638-653 - Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut:
Trend Mining in Dynamic Attributed Graphs. 654-669 - Aonan Zhang, Jun Zhu, Bo Zhang:
Sparse Relational Topic Models for Document Networks. 670-685
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