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5. ACML 2013: Canberra, ACT, Australia
- Cheng Soon Ong, Tu Bao Ho:
Asian Conference on Machine Learning, ACML 2013, Canberra, ACT, Australia, November 13-15, 2013. JMLR Workshop and Conference Proceedings 29, JMLR.org 2013
Front Matter
- Preface. 1-17
Accepted Papers
- Julien Audiffren, Hachem Kadri:
Stability of Multi-Task Kernel Regression Algorithms. 1-16 - Robert J. Durrant, Ata Kabán:
Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. 17-32 - Jelle Van Eyck, Jan Ramon, Fabian Güiza Grandas, Geert Meyfroidt, Maurice Bruynooghe, Greta Van den Berghe:
Guided Monte Carlo Tree Search for Planning in Learned Environments. 33-47 - Sholeh Forouzan, Alexander Ihler:
Linear Approximation to ADMM for MAP inference. 48-61 - Fabian Gieseke, Tapio Pahikkala, Christian Igel:
Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. 62-71 - Tobias Glasmachers, Ürün Dogan:
Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. 72-86 - Xiaotian Jiang, Zhendong Niu, Jiamin Guo, Ghulam Mustafa, Zi-Han Lin, Baomi Chen, Qian Zhou:
Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering. 87-99 - Junpei Komiyama, Issei Sato, Hiroshi Nakagawa:
Multi-armed Bandit Problem with Lock-up Periods. 116-132 - Risheng Liu, Zhouchen Lin, Zhixun Su:
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning. 116-132 - Tu Dinh Nguyen, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine. 133-148 - Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda:
Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes. 149-164 - Jaakko Peltonen, Ziyuan Lin:
Information Retrieval Perspective to Meta-visualization. 165-180 - Kazuho Watanabe, Teemu Roos, Petri Myllymäki:
Achievability of Asymptotic Minimax Regret in Online and Batch Prediction. 181-196 - Le Wu, Min-Ling Zhang:
Multi-Label Classification with Unlabeled Data: An Inductive Approach. 197-212 - Mayank Daswani, Peter Sunehag, Marcus Hutter:
Q-learning for history-based reinforcement learning. 213-228 - Marco Fornoni, Barbara Caputo, Francesco Orabona:
Multiclass Latent Locally Linear Support Vector Machines. 229-244 - Nicolas Galichet, Michèle Sebag, Olivier Teytaud:
Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits. 245-260 - Hachem Kadri, Stéphane Ayache, Cécile Capponi, Sokol Koço, François-Xavier Dupé, Emilie Morvant:
The Multi-Task Learning View of Multimodal Data. 261-276 - Sokol Koço, Cécile Capponi:
On multi-class classification through the minimization of the confusion matrix norm. 277-292 - Tam Le, Marco Cuturi:
Generalized Aitchison Embeddings for Histograms. 293-308 - Ugo Louche, Liva Ralaivola:
Unconfused Ultraconservative Multiclass Algorithms. 309-324 - Jing Lu, Steven C. H. Hoi, Jialei Wang:
Second Order Online Collaborative Filtering. 325-340 - Karim T. Abou-Moustafa, Dale Schuurmans, Frank P. Ferrie:
Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning. 341-356 - Marion Neumann, Roman Garnett, Kristian Kersting:
Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels. 357-372 - Mindika Premachandra, Mark D. Reid:
Aggregating Predictions via Sequential Mini-Trading. 373-387 - Wei-Yuan Shen, Hsuan-Tien Lin:
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking. 388-403 - Hongyu Su, Juho Rousu:
Multilabel Classification through Random Graph Ensembles. 404-418 - An Cong Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsland:
Improving Predictive Specificity of Description Logic Learners by Fortification. 419-434 - Thomas Vanck, Jochen Garcke:
Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift. 435-450 - Joseph Wang, Venkatesh Saligrama:
Locally-Linear Learning Machines (L3M). 451-466 - Wei Wang, Zhi-Hua Zhou:
Co-Training with Insufficient Views. 467-482 - Christian Wirth, Johannes Fürnkranz:
EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning. 483-497
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