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2020 – today
- 2024
[c40]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: 1-6- [c39]Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel:
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. FAccT 2024: 1763-1773 - [c38]Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen:
Generalized Schrödinger Bridge Matching. ICLR 2024 - [c37]Maximilian Nickel:
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems. NeurIPS 2024 - [i36]Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel:
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. CoRR abs/2406.01611 (2024) - [i35]Maximilian Nickel:
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems. CoRR abs/2411.13653 (2024) - [i34]Da Ju, Adina Williams, Brian Karrer, Maximilian Nickel:
Sense and Sensitivity: Evaluating the simulation of social dynamics via Large Language Models. CoRR abs/2412.05093 (2024) - 2023
- [c36]David Liu, Virginie Do, Nicolas Usunier, Maximilian Nickel:
Group fairness without demographics using social networks. FAccT 2023: 1432-1449 - [c35]Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le:
Flow Matching for Generative Modeling. ICLR 2023 - [c34]Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam:
Hyperbolic Image-text Representations. ICML 2023: 7694-7731 - [c33]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: 9814-9826 - [c32]Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman:
On Kinetic Optimal Probability Paths for Generative Models. ICML 2023: 30883-30907 - [i33]Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam:
Hyperbolic Image-Text Representations. CoRR abs/2304.09172 (2023) - [i32]David Liu, Virginie Do, Nicolas Usunier, Maximilian Nickel:
Group fairness without demographics using social networks. CoRR abs/2305.11361 (2023) - [i31]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 abs/2306.06062 (2023) - [i30]Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman:
On Kinetic Optimal Probability Paths for Generative Models. CoRR abs/2306.06626 (2023) - [i29]Arjun Subramonian, Adina Williams, Maximilian Nickel, Yizhou Sun, Levent Sagun:
Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test. CoRR abs/2307.05775 (2023) - [i28]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 abs/2309.09924 (2023) - [i27]Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen:
Generalized Schrödinger Bridge Matching. CoRR abs/2310.02233 (2023) - [i26]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 abs/2312.04823 (2023) - 2022
- [c31]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. BMVC 2022: 134 - [c30]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: 1749-1763 - [c29]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. NeurIPS 2022 - [i25]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 abs/2203.06215 (2022) - [i24]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Semi-Discrete Normalizing Flows through Differentiable Tessellation. CoRR abs/2203.06832 (2022) - [i23]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 abs/2207.04711 (2022) - [i22]Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le:
Flow Matching for Generative Modeling. CoRR abs/2210.02747 (2022) - [i21]Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich:
Latent Discretization for Continuous-time Sequence Compression. CoRR abs/2212.13659 (2022) - 2021
- [c28]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. ICLR 2021 - [c27]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. ICLR 2021 - [c26]Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake:
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. ICML 2021: 10519-10529 - [c25]Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman:
Moser Flow: Divergence-based Generative Modeling on Manifolds. NeurIPS 2021: 17669-17680 - [c24]Maximilian Nickel, Matthew Le:
Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes. WWW 2021: 706-717 - [i20]Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman:
Moser Flow: Divergence-based Generative Modeling on Manifolds. CoRR abs/2108.08052 (2021) - 2020
- [c23]Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. NeurIPS 2020 - [i19]Maximilian Nickel, Matthew Le:
Learning Multivariate Hawkes Processes at Scale. CoRR abs/2002.12501 (2020) - [i18]Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. CoRR abs/2006.10605 (2020) - [i17]Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake:
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. CoRR abs/2010.02855 (2020) - [i16]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Learning Neural Event Functions for Ordinary Differential Equations. CoRR abs/2011.03902 (2020) - [i15]Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel:
Neural Spatio-Temporal Point Processes. CoRR abs/2011.04583 (2020)
2010 – 2019
- 2019
- [c22]Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel:
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings. ACL (1) 2019: 3231-3241 - [c21]Matthew Le, Y-Lan Boureau, Maximilian Nickel:
Revisiting the Evaluation of Theory of Mind through Question Answering. EMNLP/IJCNLP (1) 2019: 5871-5876 - [c20]Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato:
Task-Driven Modular Networks for Zero-Shot Compositional Learning. ICCV 2019: 3592-3601 - [c19]Qi Liu, Maximilian Nickel, Douwe Kiela:
Hyperbolic Graph Neural Networks. NeurIPS 2019: 8228-8239 - [i14]Matt Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, Maximilian Nickel:
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings. CoRR abs/1902.00913 (2019) - [i13]Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato:
Task-Driven Modular Networks for Zero-Shot Compositional Learning. CoRR abs/1905.05908 (2019) - [i12]Qi Liu, Maximilian Nickel, Douwe Kiela:
Hyperbolic Graph Neural Networks. CoRR abs/1910.12892 (2019) - 2018
- [c18]Stephen Roller, Douwe Kiela, Maximilian Nickel:
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora. ACL (2) 2018: 358-363 - [c17]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CVPR 2018: 5919-5927 - [c16]Maximilian Nickel, Douwe Kiela:
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. ICML 2018: 3776-3785 - [c15]Douwe Kiela, Alexis Conneau, Allan Jabri, Maximilian Nickel:
Learning Visually Grounded Sentence Representations. NAACL-HLT 2018: 408-418 - [r2]Volker Tresp, Maximilian Nickel:
Relational Models. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i11]Stephen Roller, Douwe Kiela, Maximilian Nickel:
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora. CoRR abs/1806.03191 (2018) - [i10]Maximilian Nickel, Douwe Kiela:
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. CoRR abs/1806.03417 (2018) - 2017
- [c14]Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov:
Fast Linear Model for Knowledge Graph Embeddings. AKBC@NIPS 2017 - [c13]Maximilian Nickel, Douwe Kiela:
Poincaré Embeddings for Learning Hierarchical Representations. NIPS 2017: 6338-6347 - [i9]Maximilian Nickel, Douwe Kiela:
Poincaré Embeddings for Learning Hierarchical Representations. CoRR abs/1705.08039 (2017) - [i8]Théo Trouillon, Maximilian Nickel:
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison. CoRR abs/1707.01475 (2017) - [i7]Douwe Kiela, Alexis Conneau, Allan Jabri, Maximilian Nickel:
Learning Visually Grounded Sentence Representations. CoRR abs/1707.06320 (2017) - [i6]Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov:
Fast Linear Model for Knowledge Graph Embeddings. CoRR abs/1710.10881 (2017) - [i5]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CoRR abs/1711.09825 (2017) - 2016
- [j2]Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich:
A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE 104(1): 11-33 (2016) - [c12]Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio:
Holographic Embeddings of Knowledge Graphs. AAAI 2016: 1955-1961 - [i4]Volker Tresp, Maximilian Nickel:
Relational Models. CoRR abs/1609.03145 (2016) - 2015
- [i3]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 abs/1503.00759 (2015) - [i2]Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio:
Holographic Embeddings of Knowledge Graphs. CoRR abs/1510.04935 (2015) - 2014
- [j1]Yi Huang, Volker Tresp, Maximilian Nickel, Achim Rettinger, Hans-Peter Kriegel:
A scalable approach for statistical learning in semantic graphs. Semantic Web 5(1): 5-22 (2014) - [c11]Denis Krompass, Maximilian Nickel, Volker Tresp:
Large-scale factorization of type-constrained multi-relational data. DSAA 2014: 18-24 - [c10]Maximilian Nickel, Xueyan Jiang, Volker Tresp:
Reducing the Rank in Relational Factorization Models by Including Observable Patterns. NIPS 2014: 1179-1187 - [c9]Denis Krompass, Xueyan Jiang, Maximilian Nickel, Volker Tresp:
Probabilistic Latent-Factor Database Models. LD4KD 2014 - [c8]Denis Krompaß, Maximilian Nickel, Volker Tresp:
Querying Factorized Probabilistic Triple Databases. ISWC (2) 2014: 114-129 - [p1]Volker Tresp, Yi Huang, Maximilian Nickel:
Querying the Web with Statistical Machine Learning. Towards the Internet of Services 2014: 225-234 - [r1]Volker Tresp, Maximilian Nickel:
Relational Models. Encyclopedia of Social Network Analysis and Mining 2014: 1550-1561 - 2013
- [b1]Maximilian Nickel:
Tensor factorization for relational learning. Ludwig Maximilians University Munich, 2013, pp. 1-145 - [c7]Maximilian Nickel, Volker Tresp:
An Analysis of Tensor Models for Learning on Structured Data. ECML/PKDD (2) 2013: 272-287 - [c6]Maximilian Nickel, Volker Tresp:
Tensor Factorization for Multi-relational Learning. ECML/PKDD (3) 2013: 617-621 - [i1]Maximilian Nickel, Volker Tresp:
Logistic Tensor Factorization for Multi-Relational Data. CoRR abs/1306.2084 (2013) - 2012
- [c5]Xueyan Jiang, Yi Huang, Maximilian Nickel, Volker Tresp:
Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction. ESWC 2012: 164-178 - [c4]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: 601-616 - [c3]Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel:
Link Prediction in Multi-relational Graphs using Additive Models. SeRSy 2012: 1-12 - [c2]Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel:
Factorizing YAGO: scalable machine learning for linked data. WWW 2012: 271-280 - 2011
- [c1]Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel:
A Three-Way Model for Collective Learning on Multi-Relational Data. ICML 2011: 809-816
Coauthor Index
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