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Ulrich Paquet
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2020 – today
- 2023
- [i13]Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim:
Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero. CoRR abs/2310.16410 (2023) - 2022
- [j5]Nenad Tomasev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik:
Reimagining chess with AlphaZero. Commun. ACM 65(2): 60-66 (2022) - [c23]Elizabeth Bondi
, Raphael Koster, Hannah Sheahan, Martin J. Chadwick, Yoram Bachrach, A. Taylan Cemgil, Ulrich Paquet, Krishnamurthy Dvijotham:
Role of Human-AI Interaction in Selective Prediction. AAAI 2022: 5286-5294 - 2021
- [i12]Thomas McGrath, Andrei Kapishnikov, Nenad Tomasev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik:
Acquisition of Chess Knowledge in AlphaZero. CoRR abs/2111.09259 (2021) - [i11]Elizabeth Bondi, Raphael Koster, Hannah Sheahan, Martin J. Chadwick, Yoram Bachrach, A. Taylan Cemgil, Ulrich Paquet, Krishnamurthy Dvijotham:
Role of Human-AI Interaction in Selective Prediction. CoRR abs/2112.06751 (2021) - 2020
- [i10]Nenad Tomasev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik:
Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess. CoRR abs/2009.04374 (2020)
2010 – 2019
- 2019
- [i9]Silvia Chiappa, Ulrich Paquet:
Unsupervised Separation of Dynamics from Pixels. CoRR abs/1907.12906 (2019) - 2018
- [c22]Rasmus Berg Palm, Ulrich Paquet, Ole Winther:
Recurrent Relational Networks. NeurIPS 2018: 3372-3382 - [i8]Ulrich Paquet, Marco Fraccaro:
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models. CoRR abs/1810.11893 (2018) - [i7]Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman:
A Factorial Mixture Prior for Compositional Deep Generative Models. CoRR abs/1812.07480 (2018) - 2017
- [c21]Yoad Lewenberg, Yoram Bachrach, Ulrich Paquet, Jeffrey S. Rosenschein:
Knowing What to Ask: A Bayesian Active Learning Approach to the Surveying Problem. AAAI 2017: 1396-1402 - [c20]Mike Gartrell, Ulrich Paquet, Noam Koenigstein:
Low-Rank Factorization of Determinantal Point Processes. AAAI 2017: 1912-1918 - [c19]Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther:
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. NIPS 2017: 3601-3610 - [c18]Shay Ben-Elazar, Gal Lavee, Noam Koenigstein
Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation. WSDM 2017: 445-453 - [i6]Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther:
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. CoRR abs/1710.05741 (2017) - [i5]Rasmus Berg Palm
Recurrent Relational Networks for Complex Relational Reasoning. CoRR abs/1711.08028 (2017) - 2016
- [c17]Weinan Zhang, Ulrich Paquet, Katja Hofmann:
Collective Noise Contrastive Estimation for Policy Transfer Learning. AAAI 2016: 1408-1414 - [c16]Marco Fraccaro, Ulrich Paquet, Ole Winther:
Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search. AAAI 2016: 1554-1560 - [c15]Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther:
Sequential Neural Models with Stochastic Layers. NIPS 2016: 2199-2207 - [c14]Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Bayesian Low-Rank Determinantal Point Processes. RecSys 2016: 349-356 - [c13]Oren Sar Shalom, Noam Koenigstein
Beyond Collaborative Filtering: The List Recommendation Problem. WWW 2016: 63-72 - [i4]Mike Gartrell, Ulrich Paquet, Noam Koenigstein:
Low-Rank Factorization of Determinantal Point Processes for Recommendation. CoRR abs/1602.05436 (2016) - [i3]Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther:
Sequential Neural Models with Stochastic Layers. CoRR abs/1605.07571 (2016) - [i2]Mike Gartrell, Ulrich Paquet, Noam Koenigstein:
The Bayesian Low-Rank Determinantal Point Process Mixture Model. CoRR abs/1608.04245 (2016) - 2014
- [c12]Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach
Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces. RecSys 2014: 257-264 - [c11]Allison June-Barlow Chaney, Mike Gartrell, Jake M. Hofman, John Guiver, Noam Koenigstein
A large-scale exploration of group viewing patterns. TVX 2014: 31-38 - 2013
- [j4]Manfred Opper, Ulrich Paquet, Ole Winther:
Perturbative corrections for approximate inference in Gaussian latent variable models. J. Mach. Learn. Res. 14(1): 2857-2898 (2013) - [c10]Noam Koenigstein
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection. RecSys 2013: 129-136 - [c9]Ulrich Paquet, Noam Koenigstein
One-class collaborative filtering with random graphs. WWW 2013: 999-1008 - [i1]Ulrich Paquet, Noam Koenigstein:
One-class Collaborative Filtering with Random Graphs: Annotated Version. CoRR abs/1309.6786 (2013) - 2012
- [j3]Ulrich Paquet, Blaise Thomson, Ole Winther
A hierarchical model for ordinal matrix factorization. Stat. Comput. 22(4): 945-957 (2012) - [c8]Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas:
Transparent user models for personalization. KDD 2012: 678-686 - [c7]Tim Salimans, Ulrich Paquet, Thore Graepel:
Collaborative learning of preference rankings. RecSys 2012: 261-264 - [c6]Noam Koenigstein
The Xbox recommender system. RecSys 2012: 281-284
2000 – 2009
- 2009
- [j2]Ulrich Paquet, Ole Winther, Manfred Opper:
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. J. Mach. Learn. Res. 10: 1263-1304 (2009) - [c5]Ulrich Paquet:
Convexity and Bayesian constrained local models. CVPR 2009: 1193-1199 - 2008
- [c4]Manfred Opper, Ulrich Paquet, Ole Winther:
Improving on Expectation Propagation. NIPS 2008: 1241-1248 - 2007
- [b1]Ulrich Paquet:
Bayesian inference for latent variable models. University of Cambridge, UK, 2007 - [j1]Ulrich Paquet, Andries P. Engelbrecht:
Particle Swarms for Linearly Constrained Optimisation. Fundam. Informaticae 76(1-2): 147-170 (2007) - 2005
- [c3]Ulrich Paquet, Sean B. Holden, Andrew Naish-Guzman:
Bayesian Hierarchical Ordinal Regression. ICANN (2) 2005: 267-272 - [c2]Andrew Naish-Guzman, Sean B. Holden, Ulrich Paquet:
On the Explicit Use of Example Weights in the Construction of Classifiers. ICANN (2) 2005: 307-312 - 2003
- [c1]Ulrich Paquet, Andries P. Engelbrecht:
A new particle swarm optimiser for linearly constrained optimisation. IEEE Congress on Evolutionary Computation 2003: 227-233
Coauthor Index
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