Best ML Paper

The Best Student Paper Award (Machine Learning) is selected by the Awards Chairs, after nomination from the Program Chairs, from the submitted Machine Learning papers that involve at least one student author. The award is sponsored by the Springer Machine Learning journal.

Agnostic feature selection

Guillaume Doquet (TAU CNRS - INRIA - LRI - Université Paris-Saclay), Michèle Sebag (TAU CNRS - INRIA - LRI - Université Paris-Saclay)

Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features efficiently permit to predict the (unknown) target variable. Another setting is proposed in this paper: the selected features aim to efficiently recover the whole dataset. The proposed algorithm, called AgnoS, combines an AutoEncoder with structural regularizations to sidestep the combinatorial optimization problem at the core of feature selection. The extensive experimental validation of AgnoS on the scikit-feature benchmark suite demonstrates its ability compared to the state of the art, both in terms of supervised learning and data compression.

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Best DM Paper

The Best Student Paper Award (Data Mining) is selected by the Awards Chairs, after nomination from the Program Chairs, from the submitted Data Mining papers that involve at least one student author. The award is sponsored by the Springer Data Mining and Knowledge Discovery journal.

FastPoint: Scalable Deep Point Processes

Ali Caner Türkmen (Boğaziçi University), Yuyang Wang (Amazon Research), Alexander J. Smola (Amazon Research)

We propose FastPoint, a novel multivariate point process that enables fast and accurate learning and inference. FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each mark are modeled with Hawkes processes. This results in substantially more efficient learning and scales to millions of correlated marks with superior predictive accuracy. Our construction also allows for efficient and parallel sequential Monte Carlo sampling for fast predictive inference. FastPoint outperforms baseline methods in prediction tasks on synthetic and real-world high-dimensional event data at a small fraction of the computational cost.

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Test of Time Award 2019 at ECML PKDD 2009

The Test of Time Award honors the paper from ECML PKDD 2009 with the highest impact in the field. The award is sponsored by European Research Center for Information Systems (ERCIS).

Classifier Chains for Multi-label Classification

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank (The University of Waikato)

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance- based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

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Praise by the Award Chairs: The well-known transformation of a multi-label problem into a binary problem for each label has the disadvantage that the labels might correlate. Passing label correlation information along a chain of classifiers counteracts this deficiency while maintaining the ease of implementation and understanding. The paper is extremely well recognised by 537 citations of the conference paper and 981 of the subsequent journal version.

Journal Track Reviewer Award

The journal track chairs have selected 15 reviewers, who stood out in terms of reviewing load, quality and timely completion, for a “Reviewer Award” of the Journal Track:

  • Nico Piatkowski
  • Bastian Rieck
  • Nick Vannieuwenhoven
  • Mark Last
  • Celine Vens
  • Jaakko Hollmén
  • Mahito Sugiyama
  • Matthias Schubert
  • Mykola Pechenizkiy
  • Ernestina Menasalvas
  • Panayiotis Tsaparas
  • Indre Zliobaite
  • Kai Puolamäki
  • Marko Robnik-Sikonja
  • Stefano Ferilli