Maximilian Nickel

According to our database, Maximilian Nickel authored at least 41 papers between 2011 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

Semi-Discrete Normalizing Flows through Differentiable Tessellation.

Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

CoRR, 2022

Can I see an Example? Active Learning the Long Tail of Attributes and Relations.

Tyler L. Hayes, Maximilian Nickel, Christopher Kanan, Ludovic Denoyer, Arthur Szlam

CoRR, 2022

2021

Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes.

Maximilian Nickel, Matthew Le

Proceedings of the WWW '21: The Web Conference 2021, 2021

Moser Flow: Divergence-based Generative Modeling on Manifolds.

Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

CURI: A Benchmark for Productive Concept Learning Under Uncertainty.

Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake

Proceedings of the 38th International Conference on Machine Learning, 2021

Neural Spatio-Temporal Point Processes.

Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Neural Event Functions for Ordinary Differential Equations.

Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning Multivariate Hawkes Processes at Scale.

Maximilian Nickel, Matthew Le

CoRR, 2020

Riemannian Continuous Normalizing Flows.

Emile Mathieu, Maximilian Nickel

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Hyperbolic Graph Neural Networks.

Qi Liu, Maximilian Nickel, Douwe Kiela

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Task-Driven Modular Networks for Zero-Shot Compositional Learning.

Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Revisiting the Evaluation of Theory of Mind through Question Answering.

Matthew Le, Y-Lan Boureau, Maximilian Nickel

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings.

Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Relational Models.

Volker Tresp, Maximilian Nickel

Proceedings of the Encyclopedia of Social Network Analysis and Mining, 2nd Edition, 2018

Learning Visually Grounded Sentence Representations.

Douwe Kiela, Alexis Conneau, Allan Jabri, Maximilian Nickel

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry.

Maximilian Nickel, Douwe Kiela

Proceedings of the 35th International Conference on Machine Learning, 2018

Separating Self-Expression and Visual Content in Hashtag Supervision.

Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora.

Stephen Roller, Douwe Kiela, Maximilian Nickel

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

Complex and Holographic Embeddings of Knowledge Graphs: A Comparison.

Théo Trouillon, Maximilian Nickel

CoRR, 2017

Poincaré Embeddings for Learning Hierarchical Representations.

Maximilian Nickel, Douwe Kiela

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Fast Linear Model for Knowledge Graph Embeddings.

Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov

Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017

2016

A Review of Relational Machine Learning for Knowledge Graphs.

Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich

Proc. IEEE, 2016

Relational Models.

Volker Tresp, Maximilian Nickel

CoRR, 2016

Holographic Embeddings of Knowledge Graphs.

Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction.

Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich

CoRR, 2015

2014

Querying the Web with Statistical Machine Learning.

Volker Tresp, Yi Huang, Maximilian Nickel

Proceedings of the Towards the Internet of Services: The THESEUS Research Program, 2014

Relational Models.

Volker Tresp, Maximilian Nickel

Encyclopedia of Social Network Analysis and Mining, 2014

A scalable approach for statistical learning in semantic graphs.

Yi Huang, Volker Tresp, Maximilian Nickel, Achim Rettinger, Hans-Peter Kriegel

Semantic Web, 2014

Querying Factorized Probabilistic Triple Databases.

Denis Krompaß, Maximilian Nickel, Volker Tresp

Proceedings of the Semantic Web - ISWC 2014, 2014

Probabilistic Latent-Factor Database Models.

Denis Krompass, Xueyan Jiang, Maximilian Nickel, Volker Tresp

Proceedings of the 1st Workshop on Linked Data for Knowledge Discovery co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), 2014

Reducing the Rank in Relational Factorization Models by Including Observable Patterns.

Maximilian Nickel, Xueyan Jiang, Volker Tresp

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Large-scale factorization of type-constrained multi-relational data.

Denis Krompass, Maximilian Nickel, Volker Tresp

Proceedings of the International Conference on Data Science and Advanced Analytics, 2014

2013

Tensor factorization for relational learning.

Maximilian Nickel

PhD thesis, 2013

Logistic Tensor Factorization for Multi-Relational Data.

Maximilian Nickel, Volker Tresp

CoRR, 2013

Tensor Factorization for Multi-relational Learning.

Maximilian Nickel, Volker Tresp

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

An Analysis of Tensor Models for Learning on Structured Data.

Maximilian Nickel, Volker Tresp

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

2012

Factorizing YAGO: scalable machine learning for linked data.

Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel

Proceedings of the 21st World Wide Web Conference 2012, 2012

Link Prediction in Multi-relational Graphs using Additive Models.

Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel

Proceedings of the International Workshop on Semantic Technologies meet Recommender Systems & Big Data, 2012

Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information.

Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel, Hans-Peter Kriegel

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction.

Xueyan Jiang, Yi Huang, Maximilian Nickel, Volker Tresp

Proceedings of the Semantic Web: Research and Applications, 2012

2011

A Three-Way Model for Collective Learning on Multi-Relational Data.

Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel

Proceedings of the 28th International Conference on Machine Learning, 2011