ACML 2013

32 papers

Accelerated Coordinate Descent with Adaptive Coordinate Frequencies Tobias Glasmachers, Urun Dogan

Achievability of Asymptotic Minimax Regret in Online and Batch Prediction Kazuho Watanabe, Teemu Roos, Petri Myllymäki

Active Sampling of Pairs and Points for Large-Scale Linear Bipartite Ranking Wei-Yuan Shen, Hsuan-Tien Lin

Aggregating Predictions via Sequential Mini-Trading Mindika Premachandra, Mark Reid

Co-Training with Insufficient Views Wei Wang, Zhi-Hua Zhou

Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels Marion Neumann, Roman Garnett, Kristian Kersting

EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning Christian Wirth, Johannes Fürnkranz

Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits Nicolas Galichet, Michèle Sebag, Olivier Teytaud

Generalized Aitchison Embeddings for Histograms Tam Le, Marco Cuturi

Guided Monte Carlo Tree Search for Planning in Learned Environments Jelle Van Eyck, Jan Ramon, Fabian Guiza, Geert MeyFroidt, Maurice Bruynooghe, Greet Van den Berghe

Improving Predictive Specificity of Description Logic Learners by Fortification An Tran, Jens Dietrich, Hans Guesgen, Stephen Marsland

Information Retrieval Perspective to Meta-Visualization Jaakko Peltonen, Ziyuan Lin

Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning Karim Abou- Moustafa, Dale Schuurmans, Frank Ferrie

Learning Parts-Based Representations with Nonnegative Restricted Boltzmann Machine Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh

Linear Approximation to ADMM for MAP Inference Sholeh Forouzan, Alexander Ihler

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning Risheng Liu, Zhouchen Lin, Zhixun Su

Locally-Linear Learning Machines (L3M) Joseph Wang, Venkatesh Saligrama

Multi-Armed Bandit Problem with Lock-up Periods Junpei Komiyama, Issei Sato, Hiroshi Nakagawa

Multi-Label Classification with Unlabeled Data: An Inductive Approach Le Wu, Min-Ling Zhang

Multiclass Latent Locally Linear Support Vector Machines Marco Fornoni, Barbara Caputo, Francesco Orabona

Multilabel Classification Through Random Graph Ensembles Hongyu Su, Juho Rousu

Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering Xiaotian Jiang, Zhendong Niu, Jiamin Guo, Ghulam Mustafa, Zihan Lin, Baomi Chen, Qian Zhou

On Multi-Class Classification Through the Minimization of the Confusion Matrix Norm Sokol Koço, Cécile Capponi

Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification Fabian Gieseke, Tapio Pahikkala, Christian Igel

Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda

Q-Learning for History-Based Reinforcement Learning Mayank Daswani, Peter Sunehag, Marcus Hutter

Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions Robert Durrant, Ata Kaban

Second Order Online Collaborative Filtering Jing Lu, Steven Hoi, Jialei Wang

Stability of Multi-Task Kernel Regression Algorithms Julien Audiffren, Hachem Kadri

The Multi-Task Learning View of Multimodal Data Hachem Kadri, Stephane Ayache, Cécile Capponi, Sokol Koço, François-Xavier Dupé, Emilie Morvant

Unconfused Ultraconservative Multiclass Algorithms Ugo Louche, Liva Ralaivola

Using Hyperbolic Cross Approximation to Measure and Compensate Covariate Shift Thomas Vanck, Jochen Garcke