DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups
Adnene Belfodil, Wouter Duivesteijn, Marc Plantevit, Sylvie Cazalens, and Philippe Lamarre
Maximal Closed Set and Half-Space Separations in Finite Closure Systems
Florian Seiffarth, Tamás Horváth, and Stefan Wrobel
A Framework for Deep Constrained Clustering - Algorithms and Advances
Hongjing Zhang, Sugato Basu, and Ian Davidson
A Framework for Parallelizing Hierarchical Clustering Methods
Silvio Lattanzi, Thomas Lavastida, Kefu Lu, and Benjamin Moseley
Unsupervised and Active Learning using Maximin-based Anomaly Detection
Zahra Ghafoori, James C. Bezdek, Christopher Leckie, and Shanika Karunasekera
The Elliptical Basis Function Data Descriptor (EBFDD) Network — A One-Class Classification Approach to Anomaly Detection
Mehran H.Z.Bazargani and Brian Mac Namee
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
Dmitry Kobak, George Linderman, Stefan Steinerberger, Yuval Kluger, and Philipp Berens
Uncovering Hidden Block Structure for Clustering
Luce le Gorrec, Sandrine Mouysset, Philip A. Knight, Iain S. Duff, and Daniel Ruiz
CatchCore: Catching Hierarchical Dense Subtensor
Wenjie Feng, Shenghua Liu, and Xueqi Cheng
Fast and Parallelizable Ranking with Outliers from Pairwise Comparisons
Sungjin Im and Mahshid Montazer Qaem
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
Riccardo Guidotti, Anna Monreale, Stan Matwin, and Dino Pedreschi
Robust Anomaly Detection in Images using Adversarial Autoencoders
Laura Beggel, Michael Pfeiffer, and Bernd Bischl
Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms
Frank Hoppner and Maximilian Jahnke
Pattern-Based Anomaly Detection in Mixed-Type Time Series
Len Feremans, Vincent Vercruyssen, Boris Cule, Wannes Meert, and Bart Goethals
k is the Magic Number — Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
Sibylle Hess and Wouter Duivesteijn
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
Victor Berger and Michele Sebag
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
Babak Hosseini and Barbara Hammer
On the Stability of Feature Selection in the Presence of Feature Correlations
Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, and Gavin Brown
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
Yu Shi, Xinwei He, Naijing Zhang, Carl Yang, and Jiawei Han
Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
Charalampos E. Tsourakakis, Tianyi Chen, Naonori Kakimura and Jakub Pachocki
Node Representation Learning for Directed Graphs
Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl, and Avishek Anand
Link Prediction via Higher-Order Motif Features
Ghadeer Abuoda, Gianmarco De Francisci Morales, and Ashraf Aboulnaga
SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation
Vijaikumar M, Shirish Shevade, and M N Murty
Graph Signal Processing for Directed Graphs based on the Hermitian Laplacian
Satoshi Furutani, Toshiki Shibahara, Mitsuaki Akiyama, Kunio Hato, and Masaki Aida
Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification
Lu Bai, Yuhang Jiao, Lixin Cui, and Edwin R. Hancock
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Di Jin, Mark Heimann, Ryan A. Rossi, and Danai Koutra
A Soft Affiliation Graph Model for Scalable Overlapping Community Detection
Nishma Laitonjam, Wěipéng Huáng, and Neil J. Hurley
Node Classification for Signed Social Networks Using Diffuse Interface Methods
Pedro Mercado, Jessica Bosch, and Martin Stoll
Triangle Completion Time Prediction using Time-conserving Embedding
Vachik S. Dave and Mohammad Al Hasan
Adjustment Criteria for Recovering Causal Effects from Missing Data
Mojdeh Saadati and Jin Tian
An Algorithm for Reducing the Number of Distinct Branching Conditions in a Decision Forest
Atsuyoshi Nakamura and Kento Sakurada
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures
Laurens Devos, Wannes Meert, and Jesse Davis
String Sanitization: A Combinatorial Approach
Giulia Bernardini, Huiping Chen, Alessio Conte, Roberto Grossi, Grigorios Loukides, Nadia Pisanti, Solon P. Pissis, and Giovanna Rosone
A Drift-based Dynamic Ensemble Members Selection using Clustering for Time Series Forecasting
Amal Saadallah, Florian Priebe, and Katharina Morik
A Differentially Private Kernel Two-Sample Test
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, and Mijung Park
Learning to Signal in the Goldilocks Zone: Improving Adversary Compliance in Security Games
Sarah Cooney, Kai Wang, Elizabeth Bondi, Thanh Nguyen, Phebe Vayanos, Hailey Winetrobe, Edward A. Cranford, Cleotilde Gonzalez, Christian Lebiere, and Milind Tambe
A Stochastic Quasi-Newton Method with Nesterov's Accelerated Gradient
S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, and Hideki Asai
Exploiting the Earth's Spherical Geometry to Geolocate Images
Mike Izbicki, Evangelos E. Papalexakis, and Vassilis J. Tsotras
Continual Rare-Class Recognition with Emerging Novel Subclasses
Hung Nguyen, Xuejian Wang, and Leman Akoglu
Unjustified Classification Regions and Counterfactual Explanations in Machine Learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki
Shift Happens: Adjusting Classifiers
Theodore Heiser, Mari-Liis Allikivi, and Meelis Kull
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Jessa Bekker, Pieter Robberechts, and Jesse Davis
Cost Sensitive Evaluation of Instance Hardness in Machine Learning
Ricardo B. C. Prud^encio
Non-parametric Bayesian Isotonic Calibration: Fighting Over-confidence in Binary Classification
Mari-Liis Allikivi and Meelis Kull
PP-PLL: Probability Propagation for Partial Label Learning
Kaiwei Sun, Zijian Min, and Jin Wang
Neural Message Passing for Multi-Label Classification
Jack Lanchantin, Arshdeep Sekhon, and Yanjun Qi
Assessing the multi-labelness of multi-label data
Laurence A. F. Park, Yi Guo, and Jesse Read
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Bin Liu and Grigorios Tsoumakas
Distributed Learning of Non-Convex Linear Models with One Round of Communication
Mike Izbicki and Christian R. Shelton
SLSGD: Secure and Efficient Distributed On-device Machine Learning
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
Robin Vogel, Aur'elien Bellet, Stephan Cl'emencc
Importance Weighted Generative Networks
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, and Sinead A. Williamson
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
Takuro Kutsuna
LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, and Marco Gori
Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye
Lena A. Jager, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, and Tobias Scheffer
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
Kei Akuzawa, Yusuke Iwasawa, and Yutaka Matsuo
Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics
Ahmed Abdelwahab and Niels Landwehr
Meta-Learning for Black-box Optimization
Vishnu TV, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, and Gautam Shroff
Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions
Wolfgang Roth, Gunther Schindler, Holger Froning, and Franz Pernkopf
Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks
Matthias Kissel and Klaus Diepold
L_0-ARM: Network Sparsification via Stochastic Binary Optimization
Yang Li and Shihao Ji
Learning with Random Learning Rates
L'eonard Blier, Pierre Wolinski, and Yann Ollivier
FastPoint: Scalable Deep Point Processes
Ali Caner Turkmen, Yuyang Wang, and Alexander J. Smola
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, and Diana Marculescu
Scalable Large Margin Gaussian Process Classification
Martin Wistuba and Ambrish Rawat
Integrating Learning and Reasoning with Deep Logic Models
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, and Marco Gori
Neural Control Variates for Monte Carlo Variance Reduction
Ruosi Wan, Mingjun Zhong, Haoyi Xiong, and Zhanxing Zhu
Data Association with Gaussian Processes
Markus Kaiser, Clemens Otte, Thomas A. Runkler, and Carl Henrik Ek
Incorporating Dependencies in Spectral Kernels for Gaussian Processes
Kai Chen, Twan van Laarhoven, Jinsong Chen, and Elena Marchiori
Deep convolutional Gaussian processes
Kenneth Blomqvist, Samuel Kaski, and Markus Heinonen
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models
Daniel F. Schmidt and Enes Makalic
Fine-Grained Explanations using Markov Logic
Khan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, and Deepak Venugopal
Unsupervised Sentence Embedding Using Document Structure-based Context
Taesung Lee and Youngja Park
Copy Mechanism and Tailored Training for Character-based Data-to-text Generation
Marco Roberti, Giovanni Bonetta, Rossella Cancelliere, and Patrick Gallinari
NSEEN: Neural Semantic Embedding for Entity Normalization in Biomedicine
Shobeir Fakhraei, Joel Mathew, and Jos'e
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts
Maarten Grootendorst and Joaquin Vanschoren
A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset
Cornelia Ferner and Stefan Wegenkittl
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
Prashanth Vijayaraghavan and Deb Roy
Deep Ordinal Reinforcement Learning
Alexander Zap, Tobias Joppen, and Johannes Furnkranz
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Denis Steckelmacher, H'el`ene Plisnier, Diederik M. Roijers, and Ann Now'e
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Quentin Debard, Jilles Steeve Dibangoye, St'ephane Canu, and Christian Wolf
Safe Policy Improvement with Soft Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, and R'emi Tachet des Combes
An Engineered Empirical Bernstein Bound
Mark A. Burgess, Archie C. Chapman, and Paul Scott
Stochastic Activation Actor Critic Methods
Wenling Shang, Douwe van der Wal, Herke van Hoof, and Max Welling
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space
Zac Wellmer and James T. Kwok
Attentive Multi-Task Deep Reinforcement Learning
Timo Bram, Gino Brunner, Oliver Richter, and Roger Wattenhofer
BelMan: An Information-Geometric Approach to Stochastic Bandits
Debabrota Basu, Pierre Senellart, and St'e
A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments
Rui Xia, Vincent Y. F. Tan, Louis Filstroff, and C'edric F'evotte
A Reduction of Label Ranking to Multiclass Classification
Klaus Brinker and Eyke Hullermeier
Learning to Calibrate and Rerank Multi-label Predictions
Cheng Li, Virgil Pavlu, Javed Aslam, Bingyu Wang, and Kechen Qin
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Marius Koppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, and Stefan Kramer
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
Aleksandra Burashnikova, Yury Maximov, and Massih-Reza Amini
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
Omid Mohamad Nezami, Mark Dras, Len Hamey, Deborah Richards, Stephen Wan, and C'ecile Paris
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation
Mark Thomas, Bruce Martin, Katie Kowarski, Briand Gaudet, and Stan Matwin
Learning Disentangled Representations of Satellite Image Time Series
Eduardo Hugo Sanchez, Mathieu Serrurier, and Mathias Ortner
Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
Kai Hou Yip, Nikolaos Nikolaou, Piero Coronica, Angelos Tsiaras, Billy Edwards, Quentin Changeat, Mario Morvan, Beth Biller, Sasha Hinkley, Jeffrey Salmond, Matthew Archer, Paul Sumption, Elodie Choquet, Remi Soummer, Laurent Pueyo, and Ingo P. Waldmann
J3R: Joint Multi-task Learning of Ratings and Review Summaries for Explainable Recommendation
Avinesh P.V.S, Yongli Ren, Christian M. Meyer, Jeffrey Chan, Zhifeng Bao, and Mark Sanderson
Augmenting Semantic Representation of Depressive Language: from Forums to Microblogs
Nawshad Farruque, Osmar Zaiane, and Randy Goebel
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel, Thomas Plagemann, Knut Liestøl, and Mohan Kankanhalli
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
Jann Goschenhofer, Franz MJ Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, and Janek Thomas
Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data
Christina Papagiannopoulou, Ren'e
CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting
Ali Mert Ertugrul, Yu-Ru Lin, and Tugba Taskaya-Temizel
Scalable Bid Landscape Forecasting in Real-time Bidding
Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu, and Viswanathan Swaminathan
A Deep Multi-Task Approach for Residual Value Forecasting
Ahmed Rashed, Shayan Jawed, Jens Rehberg, Josif Grabocka, Lars Schmidt-Thieme, and Andre Hintsches
Transfer Learning in Credit Risk
Hendra Suryanto, Charles Guan, Andrew Voumard, and Ghassan Beydoun
Cold-Start Recommendation for On-Demand Cinemas
Beibei Li, Beihong Jin, Taofeng Xue, Kunchi Liu, Qi Zhang, and Sihua Tian
Shallow Self-Learning for Reject Inference in Credit Scoring
Nikita Kozodoi, Panagiotis Katsas, Stefan Lessmann, Luis Moreira-Matias, and Konstantinos Papakonstantinou
LSTM encoder-predictor for short-term train load forecasting
Kevin Pasini, Mostepha Khouadjia, Allou Sam'e, Fabrice Ganansia, and Latifa Oukhellou
Characterization and Early Detection of Evergreen News Articles
Yiming Liao, Shugang Wang, Eui-Hong (Sam) Han, Jongwuk Lee, and Dongwon Lee
Player Vectors: Characterizing Soccer Players' Playing Style from Match Event Streams
Tom Decroos and Jesse Davis
A Semi-Supervised and Online Learning Approach for Non-Intrusive Load Monitoring
Hajer Salem and Moamar Sayed-Mouchaweh
Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks
Sushrut Bhalla, Matthew Yao, Jean-Pierre Hickey, and Mark Crowley
Generative Adversarial Networks for Failure Prediction
Shuai Zheng, Ahmed Farahat, and Chetan Gupta
Interpreting atypical conditions in systems with deep conditional Autoencoders: the case of electrical consumption
Antoine Marot, Antoine Rosin, Laure Crochepierre, Benjamin Donnot, Pierre Pinson, and Lydia Boudjeloud-Assala
Manufacturing Dispatching using Reinforcement and Transfer Learning
Shuai Zheng, Chetan Gupta, and Susumu Serita
An aggregate learning approach for interpretable semi-supervised population prediction and disaggregation using ancillary data
Guillaume Derval, Fr'ed'eric Docquier, and Pierre Schaus
Optimizing Neural Networks for Patent Classification
Louay Abdelgawad, Peter Kluegl, Erdan Genc, Stefan Falkner, and Frank Hutter
The Search for Equations – Learning to Identify Similarities between Mathematical Expressions
Lukas Pfahler, Jonathan Schill, and Katharina Morik
Data-driven Policy on Feasibility Determination for the Train Shunting Problem
Paulo Roberto de Oliveira da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, Wan-Jui Lee, and Uzay Kaymak
Automated Data Transformation with Inductive Programming and Dynamic Background Knowledge
Lidia Contreras-Ochando, C`esar Ferri, Jos'e Hern'andez-Orallo, Fernando Mart'i nez-Plumed, Mar'i a Jos'e Ram'i rez-Quintana, and Susumu Katayama
BK-ADAPT: Dynamic Background Knowledge for Automating Data Transformation
Lidia Contreras-Ochando, C`esar Ferri, Jos'e Hern'andez-Orallo, Fernando Mart'i nez-Plumed, Mar'i a Jos'e Ram'i rez-Quintana, and Susumu Katayama
A Tool for Researchers: Querying Big Scholarly Data through Graph Databases
Fabio Mercorio, Mario Mezzanzanica, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperl`i
OCADaMi: One-Class Anomaly Detection and Data Mining toolbox
Andreas Theissler, Stephan Frey, and Jens Ehlert
MatrixCalculus.org – Computing Derivatives of Matrix and Tensor Expressions
Soren Laue, Matthias Mitterreiter, and Joachim Giesen
Towards a Predictive Patent Analytics and Evaluation Platform
Nebula Alam, Khoi-Nguyen Tran, Sue Ann Chen, John Wagner, Josh Andres, and Mukesh Mohania
A Virtualized Video Surveillance System for Public Transportation
Talmaj Marinč, Serhan Gül, Cornelius Hellge, Peter Schüssler, Thomas Riegel, and Peter Amon
Distributed Algorithms to Find Similar Time Series
Oleksandra Levchenko, Bojan Kolev, Djamel-Edine Yagoubi, Dennis Shasha, Themis Palpanas, Patrick Valduriez, Reza Akbarinia, and Florent Masseglia
UnFOOT: Unsupervised Football Analytics Tool
José Carlos Coutinho, João Mendes Moreira, and Cláudio Rebelo de Sá
ISETS: Incremental Shapelet Extraction from Time Series Stream
Jingwei Zuo, Karine Zeitouni, and Yehia Taher
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