MLJ 2021

99 papers

A Comparison of Statistical Relational Learning and Graph Neural Networks for Aggregate Graph Queries Varun Embar, Sriram Srinivasan, Lise Getoor

A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding Ioannis Boukas, Damien Ernst, Thibaut Théate, Adrien Bolland, Alexandre Huynen, Martin Buchwald, Christelle Wynants, Bertrand Cornélusse

A Framework for the Fine-Grained Evaluation of the Instantaneous Expected Value of Soccer Possessions Javier Fernández, Luke Bornn, Daniel Cervone

A Unified Framework for Closed-Form Nonparametric Regression, Classification, Preference and Mixed Problems with Skew Gaussian Processes Alessio Benavoli, Dario Azzimonti, Dario Piga

Adaptive Covariate Acquisition for Minimizing Total Cost of Classification Daniel Andrade, Yuzuru Okajima

AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow Haiyan Jiang, Haoyi Xiong, Dongrui Wu, Ji Liu, Dejing Dou

Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation Srivatsan Krishnan, Behzad Boroujerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier, Willem Waegeman

An Empirical Comparison Between Stochastic and Deterministic Centroid Initialisation for K-Means Variations Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki

An Extended DEIM Algorithm for Subset Selection and Class Identification Emily P. Hendryx, Beatrice M. Riviere, Craig G. Rusin

Analysis of Regularized Least-Squares in Reproducing Kernel Kreĭn Spaces Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens

autoBOT: Evolving Neuro-Symbolic Representations for Explainable Low Resource Text Classification Blaz Skrlj, Matej Martinc, Nada Lavrac, Senja Pollak

AUTOMAT[R]IX: Learning Simple Matrix Pipelines Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo

Automated Adaptation Strategies for Stream Learning Rashid Bakirov, Damien Fay, Bogdan Gabrys

Automatic Discovery of Interpretable Planning Strategies Julian Skirzynski, Frederic Becker, Falk Lieder

Bandit Algorithms to Personalize Educational Chatbots William Cai, Josh Grossman, Zhiyuan Lin, Hao Sheng, Johnny Tian-Zheng Wei, Joseph Jay Williams, Sharad Goel

Bayesian Optimization with Approximate Set Kernels Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi

Beneficial and Harmful Explanatory Machine Learning Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid

Beyond Graph Neural Networks with Lifted Relational Neural Networks Gustav Sourek, Filip Zelezný, Ondrej Kuzelka

Challenges of Real-World Reinforcement Learning: Definitions, Benchmarks and Analysis Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

Concentration Bounds for Temporal Difference Learning with Linear Function Approximation: The Case of Batch Data and Uniform Sampling L. A. Prashanth, Nathaniel Korda, Rémi Munos

Conditional T-SNE: More Informative T-SNE Embeddings Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

Conditional Variance Penalties and Domain Shift Robustness Christina Heinze-Deml, Nicolai Meinshausen

Convex Optimization with an Interpolation-Based Projection and Its Application to Deep Learning Riad Akrour, Asma Atamna, Jan Peters

Convex Programming Based Spectral Clustering Tomohiko Mizutani

Correction to: Fast and Accurate Pseudoinverse with Sparse Matrix Reordering and Incremental Approach Jinhong Jung, Lee Sael

Coupling Matrix Manifolds Assisted Optimization for Optimal Transport Problems Dai Shi, Junbin Gao, Xia Hong, S. T. Boris Choy, Zhiyong Wang

CPAS: The UK's National Machine Learning-Based Hospital Capacity Planning System for COVID-19 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

Data Driven Conditional Optimal Transport Esteban G. Tabak, Giulio Trigila, Wenjun Zhao

Dealing with Multiple Experts and Non-Stationarity in Inverse Reinforcement Learning: An Application to Real-Life Problems Amarildo Likmeta, Alberto Maria Metelli, Giorgia Ramponi, Andrea Tirinzoni, Matteo Giuliani, Marcello Restelli

Deep Learning and Multivariate Time Series for Cheat Detection in Video Games José Pedro Pinto, André Pimenta, Paulo Novais

Density-Based Weighting for Imbalanced Regression Michael Steininger, Konstantin Kobs, Padraig Davidson, Anna Krause, Andreas Hotho

Distance Metric Learning for Graph Structured Data Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama

Early Classification of Time Series Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols, Asma Dachraoui

Efficient Weingarten mAP and Curvature Estimation on Manifolds Yueqi Cao, Didong Li, Huafei Sun, Amir H. Assadi, Shiqiang Zhang

Estimation of Multidimensional Item Response Theory Models with Correlated Latent Variables Using Variational Autoencoders Geoffrey A. Converse, Mariana Curi, Suely Oliveira, Jonathan Templin

F*: An Interpretable Transformation of the F-Measure David J. Hand, Peter Christen, Nishadi Kirielle

Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-Term Forecasting Kai Chen, Twan van Laarhoven, Elena Marchiori

Global Optimization Based on Active Preference Learning with Radial Basis Functions Alberto Bemporad, Dario Piga

Graph-Based Semi-Supervised Learning via Improving the Quality of the Graph Dynamically Jiye Liang, Junbiao Cui, Jie Wang, Wei Wei

Grounded Action Transformation for Sim-to-Real Reinforcement Learning Josiah P. Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, Peter Stone

HIVE-COTE 2.0: A New Meta Ensemble for Time Series Classification Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony J. Bagnall

How Artificial Intelligence and Machine Learning Can Help Healthcare Systems Respond to COVID-19 Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole

Importance Sampling in Reinforcement Learning with an Estimated Behavior Policy Josiah P. Hanna, Scott Niekum, Peter Stone

Imputation of Clinical Covariates in Time Series Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski

Incorporating Symbolic Domain Knowledge into Graph Neural Networks Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig

Inductive Learning of Answer Set Programs for Autonomous Surgical Task Planning Daniele Meli, Mohan Sridharan, Paolo Fiorini

Information-Theoretic Regularization for Learning Global Features by Sequential VAE Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo

IntelligentPooling: Practical Thompson Sampling for mHealth Sabina Tomkins, Peng Liao, Predrag V. Klasnja, Susan A. Murphy

Interpretable Clustering: An Optimization Approach Dimitris Bertsimas, Agni Orfanoudaki, Holly M. Wiberg

Inverse Reinforcement Learning in Contextual MDPs Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy

Joint Optimization of an Autoencoder for Clustering and Embedding Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen

Kernel Machines for Current Status Data Yael Travis-Lumer, Yair Goldberg

Large Scale Multi-Label Learning Using Gaussian Processes Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias

Learning Hierarchical Probabilistic Logic Programs Arnaud Nguembang Fadja, Fabrizio Riguzzi, Evelina Lamma

Learning Programs by Learning from Failures Andrew Cropper, Rolf Morel

Learning Subtree Pattern Importance for Weisfeiler-Lehman Based Graph Kernels Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka

Lessons on Off-Policy Methods from a Notification Component of a Chatbot Scott Rome, Tianwen Chen, Michael Kreisel, Ding Zhou

Linear Support Vector Regression with Linear Constraints Quentin Klopfenstein, Samuel Vaiter

LoRAS: An Oversampling Approach for Imbalanced Datasets Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer

Loss Aware Post-Training Quantization Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson

Misalignment Problem in Matrix Decomposition with Missing Values Sofia Fernandes, Mário Antunes, Diogo Gomes, Rui L. Aguiar

MLife: A Lite Framework for Machine Learning Lifecycle Initialization Cong Yang, Wenfeng Wang, Yunhui Zhang, Zhikai Zhang, Lina Shen, Yipeng Li, John See

MODES: Model-Based Optimization on Distributed Embedded Systems Junjie Shi, Jiang Bian, Jakob Richter, Kuan-Hsun Chen, Jörg Rahnenführer, Haoyi Xiong, Jian-Jia Chen

Multi-Objective Multi-Armed Bandit with Lexicographically Ordered and Satisficing Objectives Alihan Hüyük, Cem Tekin

Multiple Clusterings of Heterogeneous Information Networks Shaowei Wei, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

Node Classification over Bipartite Graphs Through Projection Marija Stankova, Stiene Praet, David Martens, Foster J. Provost

On Testing Transitivity in Online Preference Learning Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

Optimal Data Collection Design in Machine Learning: The Case of the Fixed Effects Generalized Least Squares Panel Data Model Giorgio Gnecco, Federico Nutarelli, Daniela Selvi

Ordinal Regression with Explainable Distance Metric Learning Based on Ordered Sequences Juan-Luis Suárez, Salvador García, Francisco Herrera

OWL2Vec*: Embedding of OWL Ontologies Jiaoyan Chen, Pan Hu, Ernesto Jiménez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, Ian Horrocks

Partially Observable Environment Estimation with Uplift Inference for Reinforcement Learning Based Recommendation Wenjie Shang, Qingyang Li, Zhiwei Qin, Yang Yu, Yiping Meng, Jieping Ye

Probabilistic Inductive Constraint Logic Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti, Evelina Lamma

Protect Privacy of Deep Classification Networks by Exploiting Their Generative Power Jiyu Chen, Yiwen Guo, Qianjun Zheng, Hao Chen

Provable Training Set Debugging for Linear Regression Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh

Pseudo-Marginal Bayesian Inference for Gaussian Process Latent Variable Models Charles W. L. Gadd, Sara Wade, Akeel A. Shah

QuicK-Means: Accelerating Inference for K-Means by Learning Fast Transforms Luc Giffon, Valentin Emiya, Hachem Kadri, Liva Ralaivola

RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods Shay Vargaftik, Isaac Keslassy, Ariel Orda, Yaniv Ben-Itzhak

RB-CCR: Radial-Based Combined Cleaning and Resampling Algorithm for Imbalanced Data Classification Michal Koziarski, Colin Bellinger, Michal Wozniak

Reachable Sets of Classifiers and Regression Models: (non-)robustness Analysis and Robust Training Anna-Kathrin Kopetzki, Stephan Günnemann

Regularisation of Neural Networks by Enforcing Lipschitz Continuity Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree

Reshaped Tensor Nuclear Norms for Higher Order Tensor Completion Kishan Wimalawarne, Hiroshi Mamitsuka

Robust Non-Parametric Regression via Incoherent Subspace Projections Bhaskar Mukhoty, Subhajit Dutta, Purushottam Kar

Robust Supervised Topic Models Under Label Noise Wei Wang, Bing Guo, Yan Shen, Han Yang, Yaosen Chen, Xinhua Suo

Sampled Gromov Wasserstein Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban

Sparse Classification: A Scalable Discrete Optimization Perspective Dimitris Bertsimas, Jean Pauphilet, Bart P. G. Van Parys

SPEED: Secure, PrivatE, and Efficient Deep Learning Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber, Cédric Gouy-Pailler, Renaud Sirdey

Statistical Hierarchical Clustering Algorithm for Outlier Detection in Evolving Data Streams Dalibor Krleza, Boris Vrdoljak, Mario Brcic

Tensor Decision Trees for Continual Learning from Drifting Data Streams Bartosz Krawczyk

Tensor Q-Rank: New Data Dependent Definition of Tensor Rank Hao Kong, Canyi Lu, Zhouchen Lin

Testing Conditional Independence in Supervised Learning Algorithms David S. Watson, Marvin N. Wright

The Voice of Optimization Dimitris Bertsimas, Bartolomeo Stellato

Top Program Construction and Reduction for Polynomial Time Meta-Interpretive Learning Stassa Patsantzis, Stephen H. Muggleton

Topic Extraction from Extremely Short Texts with Variational Manifold Regularization Ximing Li, Yang Wang, Jihong Ouyang, Meng Wang

Toward Optimal Probabilistic Active Learning Using a Bayesian Approach Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick

Triply Stochastic Gradient Method for Large-Scale Nonlinear Similar Unlabeled Classification Wanli Shi, Bin Gu, Xiang Li, Cheng Deng, Heng Huang

TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall Rilwan A. Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta

Variational Learning from Implicit Bandit Feedback Quoc-Tuan Truong, Hady W. Lauw

ZipLine: An Optimized Algorithm for the Elastic Bulk Synchronous Parallel Model Xing Zhao, Manos Papagelis, Aijun An, Bao Xin Chen, Junfeng Liu, Yonggang Hu