Maximilian Nickel

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 61

Top Venues

Ludwig Maximilians University Munich (PHD)

Education History

Ludwig Maximilians University Munich

PHD

Publications

Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. CoRR (2024)
Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. CISS (2024)
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
Generalized Schrödinger Bridge Matching. ICLR (2024)
Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. FAccT (2024)
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman
On Kinetic Optimal Probability Paths for Generative Models. CoRR (2023)
Dhananjay Bhaskar, Yanlei Zhang, Charles Xu, Xingzhi Sun, Oluwadamilola Fasina, Guy Wolf, Maximilian Nickel, Michael Perlmutter, Smita Krishnaswamy
Graph topological property recovery with heat and wave dynamics-based features on graphs. CoRR (2023)
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le
Flow Matching for Generative Modeling. ICLR (2023)
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
Generalized Schrödinger Bridge Matching. CoRR (2023)
David Liu, Virginie Do, Nicolas Usunier, Maximilian Nickel
Group fairness without demographics using social networks. CoRR (2023)
Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam
Hyperbolic Image-Text Representations. CoRR (2023)
Arjun Subramonian, Adina Williams, Maximilian Nickel, Yizhou Sun, Levent Sagun
Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test. CoRR (2023)
Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam
Hyperbolic Image-text Representations. ICML (2023)
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Neural FIM for learning Fisher Information Metrics from point cloud data. CoRR (2023)
David Liu, Virginie Do, Nicolas Usunier, Maximilian Nickel
Group fairness without demographics using social networks. FAccT (2023)
Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. CoRR (2023)
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman
On Kinetic Optimal Probability Paths for Generative Models. ICML (2023)
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Neural FIM for learning Fisher information metrics from point cloud data. ICML (2023)
Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich
Latent Discretization for Continuous-time Sequence Compression. CoRR (2022)
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman
Matching Normalizing Flows and Probability Paths on Manifolds. CoRR (2022)
Tyler L. Hayes, Maximilian Nickel, Christopher Kanan, Ludovic Denoyer, Arthur Szlam
Can I see an Example? Active Learning the Long Tail of Attributes and Relations. CoRR (2022)
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman
Matching Normalizing Flows and Probability Paths on Manifolds. ICML (2022)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Semi-Discrete Normalizing Flows through Differentiable Tessellation. CoRR (2022)
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
Flow Matching for Generative Modeling. CoRR (2022)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Semi-Discrete Normalizing Flows through Differentiable Tessellation. NeurIPS (2022)
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Moser Flow: Divergence-based Generative Modeling on Manifolds. CoRR (2021)
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Moser Flow: Divergence-based Generative Modeling on Manifolds. NeurIPS (2021)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Neural Spatio-Temporal Point Processes. ICLR (2021)
Maximilian Nickel, Matthew Le
Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes. WWW (2021)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Learning Neural Event Functions for Ordinary Differential Equations. ICLR (2021)
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. ICML (2021)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Learning Neural Event Functions for Ordinary Differential Equations. CoRR (2020)
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Neural Spatio-Temporal Point Processes. CoRR (2020)
Emile Mathieu, Maximilian Nickel
Riemannian Continuous Normalizing Flows. CoRR (2020)
Emile Mathieu, Maximilian Nickel
Riemannian Continuous Normalizing Flows. NeurIPS (2020)
Maximilian Nickel, Matthew Le
Learning Multivariate Hawkes Processes at Scale. CoRR (2020)
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. CoRR (2020)
Qi Liu, Maximilian Nickel, Douwe Kiela
Hyperbolic Graph Neural Networks. NeurIPS (2019)
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato
Task-Driven Modular Networks for Zero-Shot Compositional Learning. ICCV (2019)
Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings. CoRR (2019)
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato
Task-Driven Modular Networks for Zero-Shot Compositional Learning. CoRR (2019)
Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings. ACL (1) (2019)
Matthew Le, Y-Lan Boureau, Maximilian Nickel
Revisiting the Evaluation of Theory of Mind through Question Answering. EMNLP/IJCNLP (1) (2019)
Qi Liu, Maximilian Nickel, Douwe Kiela
Hyperbolic Graph Neural Networks. CoRR (2019)
Douwe Kiela, Alexis Conneau, Allan Jabri, Maximilian Nickel
Learning Visually Grounded Sentence Representations. NAACL-HLT (2018)
Maximilian Nickel, Douwe Kiela
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. CoRR (2018)
Stephen Roller, Douwe Kiela, Maximilian Nickel
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora. CoRR (2018)
Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten
Separating Self-Expression and Visual Content in Hashtag Supervision. CVPR (2018)
Maximilian Nickel, Douwe Kiela
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. ICML (2018)
Stephen Roller, Douwe Kiela, Maximilian Nickel
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora. ACL (2) (2018)
Maximilian Nickel, Douwe Kiela
Poincaré Embeddings for Learning Hierarchical Representations. NIPS (2017)
Théo Trouillon, Maximilian Nickel
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison. CoRR (2017)
Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov
Fast Linear Model for Knowledge Graph Embeddings. AKBC@NIPS (2017)
Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov
Fast Linear Model for Knowledge Graph Embeddings. CoRR (2017)
Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten
Separating Self-Expression and Visual Content in Hashtag Supervision. CoRR (2017)
Maximilian Nickel, Douwe Kiela
Poincaré Embeddings for Learning Hierarchical Representations. CoRR (2017)
Douwe Kiela, Alexis Conneau, Allan Jabri, Maximilian Nickel
Learning Visually Grounded Sentence Representations. CoRR (2017)
Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
Holographic Embeddings of Knowledge Graphs. AAAI (2016)
Volker Tresp, Maximilian Nickel
Relational Models. CoRR (2016)
Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE 104 (1) (2016)
Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
Holographic Embeddings of Knowledge Graphs. CoRR (2015)
Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. CoRR (2015)
Yi Huang, Volker Tresp, Maximilian Nickel, Achim Rettinger, Hans-Peter Kriegel
A scalable approach for statistical learning in semantic graphs. Semantic Web 5 (1) (2014)
Maximilian Nickel, Xueyan Jiang, Volker Tresp
Reducing the Rank in Relational Factorization Models by Including Observable Patterns. NIPS (2014)
Denis Krompass, Xueyan Jiang, Maximilian Nickel, Volker Tresp
Probabilistic Latent-Factor Database Models. LD4KD (2014)
Denis Krompaß, Maximilian Nickel, Volker Tresp
Querying Factorized Probabilistic Triple Databases. ISWC (2) (2014)
Denis Krompass, Maximilian Nickel, Volker Tresp
Large-scale factorization of type-constrained multi-relational data. DSAA (2014)
Maximilian Nickel, Volker Tresp
An Analysis of Tensor Models for Learning on Structured Data. ECML/PKDD (2) (2013)
Maximilian Nickel, Volker Tresp
Tensor Factorization for Multi-relational Learning. ECML/PKDD (3) (2013)
Maximilian Nickel, Volker Tresp
Logistic Tensor Factorization for Multi-Relational Data. CoRR (2013)
Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel
Link Prediction in Multi-relational Graphs using Additive Models. SeRSy (2012)
Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel
Factorizing YAGO: scalable machine learning for linked data. WWW (2012)
Xueyan Jiang, Yi Huang, Maximilian Nickel, Volker Tresp
Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction. ESWC (2012)
Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel, Hans-Peter Kriegel
Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information. ECML/PKDD (1) (2012)
Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel
A Three-Way Model for Collective Learning on Multi-Relational Data. ICML (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Maximilian Nickel