Kernel Adaptive Metropolis-Hastings. - Dino Sejdinovic; Heiko Strathmann; Maria Lomeli Garcia; Christophe Andrieu; Arthur Gretton (ICML, 2014)

A hybrid sampler for Poisson-Kingman mixture models. - Maria Lomeli; Stefano Favaro; Yee Whye Teh (NIPS, 2015)

On a class of σ-stable Poisson-Kingman models and an effective marginalized sampler. - Stefano Favaro; Maria Lomeli; Yee Whye Teh (Stat. Comput., 2015)

Universal Marginalizer for Amortised Inference and Embedding of Generative Models. - Robert Walecki; Albert Buchard; Kostis Gourgoulias; Chris Hart; Maria Lomeli; Alexandre K. W. Navarro; Max Zwiessele; Yura Perov; Saurabh Johri (CoRR, 2018)

Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs. - Robert Walecki; Kostis Gourgoulias; Adam Baker; Chris Hart; Chris Lucas 002; Max Zwiessele; Albert Buchard; Maria Lomeli; Yura Perov; Saurabh Johri (CoRR, 2019)

Antithetic and Monte Carlo kernel estimators for partial rankings. - Maria Lomeli; Mark Rowland 001; Arthur Gretton; Zoubin Ghahramani (Stat. Comput., 2019)

Masking schemes for universal marginalisers. - Divya Gautam; Maria Lomeli; Kostis Gourgoulias; Daniel H. Thompson; Saurabh Johri (CoRR, 2020)

General Latent Feature Models for Heterogeneous Datasets. - Isabel Valera; Melanie F. Pradier; Maria Lomeli; Zoubin Ghahramani (J. Mach. Learn. Res., 2020)

Facebook's Cyber-Cyber and Cyber-Physical Digital Twins. - John Ahlgren; Kinga Bojarczuk; Sophia Drossopoulou; Inna Dvortsova; Johann George; Natalija Gucevska; Mark Harman; Maria Lomeli; Simon M. M. Lucas; Erik Meijer 001; Steve Omohundro; Rubmary Rojas; et al. (EASE, 2021)

Measurement Challenges for Cyber Cyber Digital Twins: Experiences from the Deployment of Facebook's WW Simulation System. - Kinga Bojarczuk; Natalija Gucevska; Simon M. M. Lucas; Inna Dvortsova; Mark Harman; Erik Meijer 001; Silvia Sapora; Johann George; Maria Lomeli; Rubmary Rojas (ESEM, 2021)

Testing Web Enabled Simulation at Scale Using Metamorphic Testing. - John Ahlgren; Maria Eugenia Berezin; Kinga Bojarczuk; Elena Dulskyte; Inna Dvortsova; Johann George; Natalija Gucevska; Mark Harman; Maria Lomeli; Erik Meijer 001; Silvia Sapora; Justin Spahr-Summers (ICSE (SEIP), 2021)

Improving Wikipedia Verifiability with AI. - Fabio Petroni; Samuel Broscheit; Aleksandra Piktus; Patrick Lewis 001; Gautier Izacard; Lucas Hosseini; Jane Dwivedi-Yu; Maria Lomeli; Timo Schick; Pierre-Emmanuel Mazaré; Armand Joulin; Edouard Grave; et al. (CoRR, 2022)

Few-shot Learning with Retrieval Augmented Language Models. - Gautier Izacard; Patrick Lewis 001; Maria Lomeli; Lucas Hosseini; Fabio Petroni; Timo Schick; Jane Dwivedi-Yu; Armand Joulin; Sebastian Riedel 001; Edouard Grave (CoRR, 2022)

EditEval: An Instruction-Based Benchmark for Text Improvements. - Jane Dwivedi-Yu; Timo Schick; Zhengbao Jiang; Maria Lomeli; Patrick Lewis 001; Gautier Izacard; Edouard Grave; Sebastian Riedel 001; Fabio Petroni (CoRR, 2022)

Toolformer: Language Models Can Teach Themselves to Use Tools. - Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Eric Hambro; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom (NeurIPS, 2023)

Toolformer: Language Models Can Teach Themselves to Use Tools. - Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom (CoRR, 2023)

Atlas: Few-shot Learning with Retrieval Augmented Language Models. - Gautier Izacard; Patrick Lewis 001; Maria Lomeli; Lucas Hosseini; Fabio Petroni; Timo Schick; Jane Dwivedi-Yu; Armand Joulin; Sebastian Riedel 001; Edouard Grave (J. Mach. Learn. Res., 2023)

Augmented Language Models: a Survey. - Grégoire Mialon; Roberto Dessì; Maria Lomeli; Christoforos Nalmpantis; Ramakanth Pasunuru; Roberta Raileanu; Baptiste Rozière; Timo Schick; Jane Dwivedi-Yu; Asli Celikyilmaz; Edouard Grave; Yann LeCun; et al. (Trans. Mach. Learn. Res., 2023)

Improving Wikipedia verifiability with AI. - Fabio Petroni; Samuel Broscheit; Aleksandra Piktus; Patrick Lewis 001; Gautier Izacard; Lucas Hosseini; Jane Dwivedi-Yu; Maria Lomeli; Timo Schick; Michele Bevilacqua; Pierre-Emmanuel Mazaré; Armand Joulin; et al. (Nat. Mac. Intell., 2023)

TOOLVERIFIER: Generalization to New Tools via Self-Verification. - Dheeraj Mekala; Jason Weston; Jack Lanchantin; Roberta Raileanu; Maria Lomeli; Jingbo Shang; Jane Dwivedi-Yu (EMNLP (Findings), 2024)

RA-DIT: Retrieval-Augmented Dual Instruction Tuning. - Xi Victoria Lin; Xilun Chen 002; Mingda Chen; Weijia Shi; Maria Lomeli; Richard James 001; Pedro Rodriguez 001; Jacob Kahn; Gergely Szilvasy; Mike Lewis; Luke Zettlemoyer; Wen-tau Yih (ICLR, 2024)

In-Context Pretraining: Language Modeling Beyond Document Boundaries. - Weijia Shi; Sewon Min; Maria Lomeli; Chunting Zhou; Margaret Li; Xi Victoria Lin; Noah A. Smith; Luke Zettlemoyer; Wen-tau Yih; Mike Lewis (ICLR, 2024)

The Faiss library. - Matthijs Douze; Alexandr Guzhva; Chengqi Deng; Jeff Johnson 004; Gergely Szilvasy; Pierre-Emmanuel Mazaré; Maria Lomeli; Lucas Hosseini; Hervé Jégou (CoRR, 2024)

Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources. - Alisia Maria Lupidi; Carlos Gemmell; Nicola Cancedda; Jane Dwivedi-Yu; Jason Weston; Jakob N. Foerster; Roberta Raileanu; Maria Lomeli (CoRR, 2024)

Evaluation data contamination in LLMs: how do we measure it and (when) does it matter? - Aaditya K. Singh; Muhammed Yusuf Kocyigit; Andrew Poulton; David Esiobu; Maria Lomeli; Gergely Szilvasy; Dieuwke Hupkes (CoRR, 2024)

Inference-time sparse attention with asymmetric indexing. - Pierre-Emmanuel Mazaré; Gergely Szilvasy; Maria Lomeli; Francisco Massa; Naila Murray; Hervé Jégou; Matthijs Douze (CoRR, 2025)