Variational Gaussian Process Dynamical Systems. - Andreas C. Damianou; Michalis K. Titsias; Neil D. Lawrence (NIPS, 2011)

Manifold Relevance Determination. - Andreas C. Damianou; Carl Henrik Ek; Michalis K. Titsias; Neil D. Lawrence (ICML, 2012)

Deep Gaussian Processes. - Andreas C. Damianou; Neil D. Lawrence (AISTATS, 2013)

Active learning for sparse bayesian multilabel classification. - Deepak Vasisht; Andreas C. Damianou; Manik Varma; Ashish Kapoor (KDD, 2014)

Gaussian Process Models with Parallelization and GPU acceleration. - Zhenwen Dai; Andreas C. Damianou; James Hensman; Neil D. Lawrence (CoRR, 2014)

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. - Andreas C. Damianou; Michalis K. Titsias; Neil D. Lawrence (CoRR, 2014)

Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data. - Luke W. Boorman; Andreas C. Damianou; Uriel Martinez-Hernandez; Tony J. Prescott (Living Machines, 2015)

A Top-Down Approach for a Synthetic Autobiographical Memory System. - Andreas C. Damianou; Carl Henrik Ek; Luke Boorman; Neil D. Lawrence; Tony J. Prescott (Living Machines, 2015)

Semi-described and semi-supervised learning with Gaussian processes. - Andreas C. Damianou; Neil D. Lawrence (UAI, 2015)

Probabilistic consolidation of grasp experience. - Yasemin Bekiroglu; Andreas C. Damianou; Renaud Detry; Johannes A. Stork; Danica Kragic; Carl Henrik Ek (ICRA, 2016)

iCub Visual Memory Inspector: Visualising the iCub's Thoughts. - Daniel Camilleri; Andreas C. Damianou; Harry Jackson; Neil D. Lawrence; Tony J. Prescott (Living Machines, 2016)

An integrated probabilistic framework for robot perception, learning and memory. - Uriel Martinez-Hernandez; Andreas C. Damianou; Daniel Camilleri; Luke W. Boorman; Neil D. Lawrence; Tony J. Prescott (ROBIO, 2016)

A Bioinspired Approach to Vision. - Daniel Camilleri; Luke W. Boorman; Uriel Martinez-Hernandez; Andreas C. Damianou; Tony J. Prescott (TAROS, 2016)

Variational Auto-encoded Deep Gaussian Processes. - Zhenwen Dai; Andreas C. Damianou; Javier González 002; Neil D. Lawrence (ICLR (Poster), 2016)

Recurrent Gaussian Processes. - César Lincoln C. Mattos; Zhenwen Dai; Andreas C. Damianou; Jeremy Forth; Guilherme A. Barreto; Neil D. Lawrence (ICLR (Poster), 2016)

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. - Andreas C. Damianou; Michalis K. Titsias; Neil D. Lawrence (J. Mach. Learn. Res., 2016)

Preferential Bayesian Optimization. - Javier González 002; Zhenwen Dai; Andreas C. Damianou; Neil D. Lawrence (ICML, 2017)

Inverse Reinforcement Learning via Deep Gaussian Process. - Ming Jin 002; Andreas C. Damianou; Pieter Abbeel; Costas J. Spanos (UAI, 2017)

Online Constrained Model-based Reinforcement Learning. - Benjamin van Niekerk; Andreas C. Damianou; Benjamin Rosman (UAI, 2017)

Manifold Alignment Determination: finding correspondences across different data views. - Andreas C. Damianou; Neil D. Lawrence; Carl Henrik Ek (CoRR, 2017)

Leveraging Crowdsourcing Data for Deep Active Learning An Application: Learning Intents in Alexa. - Jie Yang 028; Thomas Drake; Andreas C. Damianou; Yoelle Maarek (WWW, 2018)

Deep Gaussian Processes with Convolutional Kernels. - Vinayak Kumar; Vaibhav Singh; P. K. Srijith; Andreas C. Damianou (CoRR, 2018)

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self. - Clément Moulin-Frier; Tobias Fischer 001; Maxime Petit; Grégoire Pointeau; Jordi-Ysard Puigbo; Ugo Pattacini; Sock Ching Low; Daniel Camilleri; Phuong D. H. Nguyen; Matej Hoffmann; Hyung Jin Chang; Martina Zambelli; et al. (IEEE Trans. Cogn. Dev. Syst., 2018)

Variational Information Distillation for Knowledge Transfer. - Sungsoo Ahn; Shell Xu Hu; Andreas C. Damianou; Neil D. Lawrence; Zhenwen Dai (CVPR, 2019)

Transferring Knowledge across Learning Processes. - Sebastian Flennerhag; Pablo Garcia Moreno; Neil D. Lawrence; Andreas C. Damianou (ICLR, 2019)

Deep Gaussian Processes for Multi-fidelity Modeling. - Kurt Cutajar; Mark Pullin; Andreas C. Damianou; Neil D. Lawrence; Javier González 002 (CoRR, 2019)

ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems. - Bharathan Balaji; Jordan Bell-Masterson; Enes Bilgin; Andreas C. Damianou; Pablo Moreno Garcia; Arpit Jain; Runfei Luo; Alvaro Maggiar; Balakrishnan Narayanaswamy; Chun Ye (CoRR, 2019)

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. - Shell Xu Hu; Pablo Garcia Moreno; Yang Xiao 009; Xi Shen 001; Guillaume Obozinski; Neil D. Lawrence; Andreas C. Damianou (ICLR, 2020)

Similarity of Neural Networks with Gradients. - Shuai Tang; Wesley J. Maddox; Charlie Dickens; Tom Diethe; Andreas C. Damianou (CoRR, 2020)

Fast Adaptation with Linearized Neural Networks. - Wesley J. Maddox; Shuai Tang; Pablo Garcia Moreno; Andrew Gordon Wilson; Andreas C. Damianou (AISTATS, 2021)

Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data. - Francesco Tonolini; Pablo Garcia Moreno; Andreas C. Damianou; Roderick Murray-Smith (ICLR, 2021)

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. - Andreas C. Damianou; Neil D. Lawrence; Carl Henrik Ek (J. Mach. Learn. Res., 2021)

Graph Learning for Exploratory Query Suggestions in an Instant Search System. - Enrico Palumbo; Andreas Damianou; Alice Wang; Alva Liu; Ghazal Fazelnia; Francesco Fabbri; Rui Ferreira; Fabrizio Silvestri; Hugues Bouchard; Claudia Hauff; Mounia Lalmas; Ben Carterette; et al. (CIKM, 2023)

Towards Graph Foundation Models for Personalization. - Andreas Damianou; Francesco Fabbri; Paul Gigioli; Marco De Nadai; Alice Wang; Enrico Palumbo; Mounia Lalmas (WWW (Companion Volume), 2024)

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks. - Marco De Nadai; Francesco Fabbri; Paul Gigioli; Alice Wang; Ang Li; Fabrizio Silvestri; Laura Kim; Shawn Lin; Vladan Radosavljevic; Sandeep Ghael; David Nyhan; Hugues Bouchard; et al. (WWW (Companion Volume), 2024)

Generating Query Recommendations via LLMs. - Andrea Bacciu; Enrico Palumbo; Andreas Damianou; Nicola Tonellotto; Fabrizio Silvestri (CoRR, 2024)

Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models. - Konstantina Palla; José Luis Redondo García; Claudia Hauff; Francesco Fabbri; Andreas Damianou; Henrik Lindström; Daniel R. Taber; Mounia Lalmas (FAccT, 2025)

Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval. - Enrico Palumbo; Gustavo Penha; Andreas Damianou; José Luis Redondo García; Timothy Christopher Heath; Alice Wang; Hugues Bouchard; Mounia Lalmas (CoRR, 2025)

Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models. - Bernd Huber; Ghazal Fazelnia; Andreas Damianou; Sebastian Peleato; Max Lefarov; Praveen Ravichandran; Marco De Nadai; Mounia Lalmas-Roelleke; Paul N. Bennett (CoRR, 2025)

Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge. - Francesco Fabbri; Gustavo Penha; Edoardo D'Amico; Alice Wang; Marco De Nadai; Jackie Doremus; Paul Gigioli; Andreas Damianou; Oskar Stal; Mounia Lalmas (CoRR, 2025)

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations. - Marco De Nadai; Andreas Damianou; Mounia Lalmas (CoRR, 2025)