DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (ICML, 2016)

Deep Kernel Machines via the Kernel Reparametrization Trick. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (ICLR (Workshop), 2017)

Causal Inference via Kernel Deviance Measures. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (NeurIPS, 2018)

Hierarchical Adversarially Learned Inference. - Mohamed Ishmael Belghazi; Sai Rajeswar; Olivier Mastropietro; Negar Rostamzadeh; Jovana Mitrovic; Aaron C. Courville (CoRR, 2018)

Causal Reasoning from Meta-reinforcement Learning. - Ishita Dasgupta 001; Jane X. Wang; Silvia Chiappa; Jovana Mitrovic; Pedro A. Ortega; David Raposo; Edward Hughes 001; Peter W. Battaglia; Matthew M. Botvinick; Zeb Kurth-Nelson (CoRR, 2019)

Less can be more in contrastive learning. - Jovana Mitrovic; Brian McWilliams; Mélanie Rey (ICBINB@NeurIPS, 2020)

Causally Correct Partial Models for Reinforcement Learning. - Danilo J. Rezende; Ivo Danihelka; George Papamakarios; Nan Rosemary Ke; Ray Jiang; Theophane Weber; Karol Gregor; Hamza Merzic; Fabio Viola; Jane Wang 001; Jovana Mitrovic; Frederic Besse; et al. (CoRR, 2020)

Amortized learning of neural causal representations. - Nan Rosemary Ke; Jane X. Wang; Jovana Mitrovic; Martin Szummer; Danilo J. Rezende (CoRR, 2020)

Can algorithms learn from babies? Exploring how infant learning can inform and inspire unsupervised learning algorithms. - Jelena Sucevic; Jovana Mitrovic (CogSci, 2021)

Representation Learning via Invariant Causal Mechanisms. - Jovana Mitrovic; Brian McWilliams; Jacob C. Walker; Lars Holger Buesing; Charles Blundell (ICLR, 2021)

Encoders and Ensembles for Task-Free Continual Learning. - Murray Shanahan; Christos Kaplanis; Jovana Mitrovic (CoRR, 2021)

CoBERL: Contrastive BERT for Reinforcement Learning. - Andrea Banino; Adrià Puigdomènech Badia; Jacob C. Walker; Tim Scholtes; Jovana Mitrovic; Charles Blundell (ICLR, 2022)

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? - Nenad Tomasev; Ioana Bica; Brian McWilliams; Lars Buesing; Razvan Pascanu; Charles Blundell; Jovana Mitrovic (CoRR, 2022)

Continually learning representations at scale. - Alexandre Galashov; Jovana Mitrovic; Dhruva Tirumala; Yee Whye Teh; Timothy Nguyen; Arslan Chaudhry; Razvan Pascanu (CoLLAs, 2023)

SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations. - Matko Bosnjak; Pierre Harvey Richemond; Nenad Tomasev; Florian Strub; Jacob C. Walker; Felix Hill; Lars Holger Buesing; Razvan Pascanu; Charles Blundell; Jovana Mitrovic (ICLR, 2023)

Neural Algorithmic Reasoning with Causal Regularisation. - Beatrice Bevilacqua; Kyriacos Nikiforou; Borja Ibarz; Ioana Bica; Michela Paganini; Charles Blundell; Jovana Mitrovic; Petar Velickovic (ICML, 2023)

Improving fine-grained understanding in image-text pre-training. - Ioana Bica; Anastasija Ilic; Matthias Bauer; Goker Erdogan; Matko Bosnjak; Christos Kaplanis; Alexey A. Gritsenko; Matthias Minderer; Charles Blundell; Razvan Pascanu; Jovana Mitrovic (ICML, 2024)

Synth2: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings. - Sahand Sharifzadeh; Christos Kaplanis; Shreya Pathak; Dharshan Kumaran; Anastasija Ilic; Jovana Mitrovic; Charles Blundell; Andrea Banino (CoRR, 2024)