Accepted Papers

Journal Track

  • Recurring Concept Memory Management in Data Streams: Exploiting Data Stream Concept Evolution to Improve Performance and Transparency
    Ben Halstead, Yun Sing Koh, Patricia Jean Riddle and R. Pears
  • Online Summarization of Dynamic Graphs using Subjective Interestingness for Sequential Data
    Sarang Kapoor, Dhish Kumar Saxena and Matthijs van Leeuwen
  • FuseRec: Fusing user and item homophily modeling with temporal recommender systems
    Kanika Narang, Yitong Song, Alexander Schwing and Hari Sundaram
  • Detecting virtual concept drift of regressors without ground truth values
    Emilia Oikarinen, Henri Tiittanen, Andreas Henelius and Kai Puolamäki
  • MultiETSC: Automated Machine Learning for Early Time Series Classification
    Gilles Ottervanger, Mitra Baratchi and Holger H. Hoos
  • Effective Social Post Classifiers on Top of Search Interfaces
    Ryan Rivas and Vagelis Hristidis
  • Unsupervised domain adaptation with non-stochastic missing data
    Matthieu Kirchmeyer, Patrick Gallinari, Alain Rakotomamonjy and Amin Mantrach
  • CURIE: A Cellular Automaton for Concept Drift Detection
    Jesús López, Javier Del Ser, Eneko Osaba, Albert Bifet and Francisco Herrera
  • Selego: Robust Variate Selection for Accurate Time Series Forecasting
    Manoj Tiwaskar, Yash Garg, Xinsheng Li, K. Selçuk Candan and Maria Luisa Sapino
  • CrashNet: An Encoder-Decoder Architecture to Predict Crash Test Outcomes
    Ralf Krestel, Mohamed Karim Belaid and Maximilian Rabus
  • Differentially Private Distance Learning in Categorical Data
    Elena Battaglia, Simone Celano and Ruggero Gaetano Pensa
  • K-Plex Cover Pooling for Graph Neural Networks
    Davide Bacciu, Alessio Conte, Roberto Grossi, Francesco Landolfi and Andrea Marino
  • Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping
    Matthieu Herrmann and Geoffrey I. Webb
  • VFC-SMOTE: Very Fast Continuous Synthetic Minority Oversampling for Evolving Data Streams
    Alessio Bernardo and Emanuele Della Valle
  • Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training
    Anna-Kathrin Kopetzki and Stephan Günnemann
  • Bayesian Optimization with Approximate Set Kernels
    Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kima and Seungjin Choi
  • MODES: Model-based Optimization on Distributed Embedded Systems
    Junjie Shi, Jiang Bian, Jakob Richter, Kuan-Hsun Chen, Jörg Rahnenführer, Haoyi Xiong and Jian-Jia Chen
  • Protect Privacy of Deep Classification Networks by Exploiting Their Generative Power
    Jiyu Chen, Yiwen Guo, Qianjun Zheng and Hao Chen
  • Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
    Riad Akrour, Asma Atamna and Jan Peters
  • Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
    Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl and Bernhard Sick
  • Multiple Clusterings of Heterogeneous Information Networks
    Shaowei Wei, Guoxian Yu, Jun Wang, Carlotta Domeniconi and Xiangliang Zhang
  • AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow
    Haiyan Jiang, Haoyi Xiong, Ji Liu and Dejing Dou
  • Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels
    Dai Hai Nguyen, Canh Hao Nguyen and Hiroshi Mamitsuka
  • DenseLoss: A Cost-sensitive Loss for Regression on Imbalanced Datasets
    Michael Steininger, Konstantin Kobs, Padraig Davidson, Anna Krause and Andreas Hotho
  • On Testing Transitivity in Online Preference Learning
    Björn Haddenhorst, Viktor Bengs and Eyke Hüllermeier
  • SPEED: Secure, PrivatE, and Efficient Deep learning
    Arnaud Grivet Sébert, Rafaël Pinot, Martin Zuber, Cédric Gouy-Pailler and Renaud Sirdey
  • Joint Optimization of an Autoencoder for Clustering and Embedding
    Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld and Robert Jenssen
  • Gaussian Processes with Skewed Laplace Spectral Mixture kernels for long-term forecasting
    Kai Chen, Twan van Laarhoven and Elena Marchiori
  • Provable Training Set Debugging for Linear Regression
    Xiaomin Zhang, Xiaojin Zhu and Stephen J. Wright
  • Sampled Gromov Wasserstein
    Tanguy Kerdoncuff, Rémi Emonet and Marc Sebban
  • Testing Conditional Independence in Supervised Learning Algorithms
    David Watson and Marvin N. Wright
  • Variational Learning from Implicit Bandit Feedback
    Hady W. Lauw and Quoc-Tuan Truong
  • Information-Theoretic Regularization for Learning Global Features by Sequential VAE
    Kei Akuzawa, Yusuke Iwasawa and Yutaka Matsuo
  • BROCCOLI - Overlapping and Outlier-robust Biclustering through Proximal Stochastic Gradient Descent
    Gianvito Pio, Sybille Hess, Michiel E. Hochstenbach and Michelangelo Ceci
  • Robust non-Parametric Regression via Incoherent Subspace Projections
    Bhaskar Mukhoty, Subhajit Dutta, and Purushottam Kar
  • Policy Space Identification in Configurable Environments
    Guglielmo Manneschi, Alberto Maria Mettelli and Marcello Restelli
  • Efficient Set-Valued Prediction in Multi-Class Classification
    Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński, Eyke Hüllermeier and Willem Waegeman
  • AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series
    Lin Zhang, Wenyu Zhang, Maxwell J McNeil, Nachuan Chengwang, David S Matteson and Petko Bogdanov
  • What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models
    Yifan Hu, Changwei Hu, Thanh Tran, Tejaswi Kasturi, Elizabeth Joseph and Matt Gillingham