no code implementations • 1 Feb 2024 • Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
no code implementations • 5 Dec 2023 • Alex J. Chan, José Luis Redondo García, Fabrizio Silvestri, Colm O'Donnel, Konstantina Palla
We train large language models on extensive datasets of media news and articles to create culturally attuned models.
1 code implementation • ICLR 2019 • Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.
no code implementations • ICML 2017 • Konstantina Palla, David Knowles, Zoubin Ghahramani
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP).
no code implementations • 6 Jul 2016 • Cian Naik, Francois Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
no code implementations • 17 Mar 2014 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani
We present a nonparametric prior over reversible Markov chains.
no code implementations • 13 Mar 2013 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani
The fundamental aim of clustering algorithms is to partition data points.
no code implementations • NeurIPS 2012 • Konstantina Palla, Zoubin Ghahramani, David A. Knowles
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret.
1 code implementation • 19 Jul 2012 • Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information.