ACML 2013

32 papers

EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning

Christian Wirth, Johannes Fürnkranz

Co-Training with Insufficient Views

Wei Wang, Zhi-Hua Zhou

Locally-Linear Learning Machines (L3M)

Joseph Wang, Venkatesh Saligrama

Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift

Thomas Vanck, Jochen Garcke

Improving Predictive Specificity of Description Logic Learners by Fortification

An Tran, Jens Dietrich, Hans Guesgen, Stephen Marsland

Multilabel Classification through Random Graph Ensembles

Hongyu Su, Juho Rousu

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

Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels

Marion Neumann, Roman Garnett, Kristian Kersting

Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning

Karim Abou- Moustafa, Dale Schuurmans, Frank Ferrie

Second Order Online Collaborative Filtering

Jing LU, Steven Hoi, Jialei Wang

Unconfused Ultraconservative Multiclass Algorithms

Ugo Louche, Liva Ralaivola

Generalized Aitchison Embeddings for Histograms

Tam Le, Marco Cuturi

On multi-class classification through the minimization of the confusion matrix norm

Sokol Koço, Cécile Capponi

The Multi-Task Learning View of Multimodal Data

Hachem Kadri, Stéphane Ayache, Cécile Capponi, Sokol Koço, François-Xavier Dupé

Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits

Nicolas Galichet, Michele Sebag, Olivier Teytaud

Multiclass Latent Locally Linear Support Vector Machines

Marco Fornoni, Barbara Caputo, Francesco Orabona

Q-learning for history-based reinforcement learning

Mayank Daswani, Peter Sunehag, Marcus Hutter

Multi-Label Classification with Unlabeled Data: An Inductive Approach

Le Wu, Min-ling Zhang

Achievability of Asymptotic Minimax Regret in Online and Batch Prediction

Kazuho Watanabe, Teemu Roos, Petri Myllymäki

Information Retrieval Perspective to Meta-visualization

Jaakko Peltonen, Ziyuan Lin

Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes

Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda

Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine

Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

Risheng Liu, Zhouchen Lin, Zhixun Su

Multi-armed Bandit Problem with Lock-up Periods

Junpei Komiyama, Issei Sato, Hiroshi Nakagawa

Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering

Xiaotian Jiang, Zhendong Niu, Jiamin Guo, Ghulam Mustafa, Zihan Lin

Accelerated Coordinate Descent with Adaptive Coordinate Frequencies

Tobias Glasmachers, Urun Dogan

Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification

Fabian Gieseke, Tapio Pahikkala, Christian Igel

Linear Approximation to ADMM for MAP inference

Sholeh Forouzan, Alexander Ihler

Guided Monte Carlo Tree Search for Planning in Learned Environments

Jelle Van Eyck, Jan Ramon, Fabian Guiza, Geert MeyFroidt, Maurice Bruynooghe

Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions

Robert Durrant, Ata Kaban

Stability of Multi-Task Kernel Regression Algorithms

Julien Audiffren, Hachem Kadri