Combining Non-stationary Prediction, Optimization and Mixing for Data Compression. - Christopher Mattern (CCP, 2011)

Mixing Strategies in Data Compression. - Christopher Mattern (DCC, 2012)

Linear and Geometric Mixtures - Analysis. - Christopher Mattern (DCC, 2013)

On Probability Estimation via Relative Frequencies and Discount. - Christopher Mattern (DCC, 2015)

On Probability Estimation by Exponential Smoothing. - Christopher Mattern (DCC, 2015)

On Statistical Data Compression. - Christopher Mattern (2016)

Online Learning with Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; Agnieszka Grabska-Barwinska; Christopher Mattern; Peter Toth (CoRR, 2017)

Generalized Probability Smoothing. - Christopher Mattern (DCC, 2018)

Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; David Budden; Christopher Mattern; Agnieszka Grabska-Barwinska; Peter Toth; Simon Schmitt; Marcus Hutter (CoRR, 2019)

Gated Linear Networks. - Joel Veness; Tor Lattimore; David Budden; Avishkar Bhoopchand; Christopher Mattern; Agnieszka Grabska-Barwinska; Eren Sezener; Jianan Wang; Peter Toth; Simon Schmitt; Marcus Hutter (AAAI, 2021)

Hierarchical Partitioning Forecaster. - Christopher Mattern (CoRR, 2023)

Language Modeling Is Compression. - Grégoire Delétang; Anian Ruoss; Paul-Ambroise Duquenne; Elliot Catt; Tim Genewein; Christopher Mattern; Jordi Grau-Moya; Li Kevin Wenliang; Matthew Aitchison; Laurent Orseau; Marcus Hutter; Joel Veness (ICLR, 2024)

Learning Universal Predictors. - Jordi Grau-Moya; Tim Genewein; Marcus Hutter; Laurent Orseau; Grégoire Delétang; Elliot Catt; Anian Ruoss; Li Kevin Wenliang; Christopher Mattern; Matthew Aitchison; Joel Veness (ICML, 2024)