TuckER: Tensor Factorization for Knowledge Graph Completion. - Ivana Balazevic; Carl Allen; Timothy M. Hospedales (EMNLP/IJCNLP (1), 2019)

Hypernetwork Knowledge Graph Embeddings. - Ivana Balazevic; Carl Allen; Timothy M. Hospedales (ICANN (Workshop), 2019)

Analogies Explained: Towards Understanding Word Embeddings. - Carl Allen; Timothy M. Hospedales (ICML, 2019)

What the Vec? Towards Probabilistically Grounded Embeddings. - Carl Allen; Ivana Balazevic; Timothy M. Hospedales (NeurIPS, 2019)

Multi-relational Poincaré Graph Embeddings. - Ivana Balazevic; Carl Allen; Timothy M. Hospedales (NeurIPS, 2019)

On Understanding Knowledge Graph Representation. - Carl Allen; Ivana Balazevic; Timothy M. Hospedales (CoRR, 2019)

Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings. - David Chang; Ivana Balazevic; Carl Allen; Daniel Chawla; Cynthia Brandt; Richard Andrew Taylor (BioNLP, 2020)

A Probabilistic Framework for Discriminative and Neuro-Symbolic Semi-Supervised Learning. - Carl Allen; Ivana Balazevic; Timothy M. Hospedales (CoRR, 2020)

Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows. - Ivana Balazevic; Carl Allen; Timothy M. Hospedales (CoRR, 2020)

Interpreting Knowledge Graph Relation Representation from Word Embeddings. - Carl Allen; Ivana Balazevic; Timothy M. Hospedales (ICLR, 2021)

Multi-Scale attributed node embedding. - Benedek Rozemberczki; Carl Allen; Rik Sarkar (J. Complex Networks, 2021)

Adapters for Enhanced Modeling of Multilingual Knowledge and Text. - Yifan Hou; Wenxiang Jiao; Meizhen Liu 001; Carl Allen; Zhaopeng Tu; Mrinmaya Sachan (EMNLP (Findings), 2022)

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. - Djordje Miladinovic; Kumar Shridhar; Kushal Jain; Max B. Paulus; Joachim M. Buhmann; Carl Allen (NeurIPS, 2022)

Towards a Theoretical Understanding of Word and Relation Representation. - Carl Allen (CoRR, 2022)

Variational Classification. - Shehzaad Dhuliawala; Mrinmaya Sachan; Carl Allen (CoRR, 2023)

A Probabilistic Model to explain Self-Supervised Representation Learning. - Alice Bizeul; Bernhard Schölkopf; Carl Allen (CoRR, 2024)

Unpicking Data at the Seams: VAEs, Disentanglement and Independent Components. - Carl Allen (CoRR, 2024)

A Probabilistic Model behind Self- Supervised Learning. - Alice Bizeul; Bernhard Schölkopf; Carl Allen (Trans. Mach. Learn. Res., 2024)

Variational Classification: A Probabilistic Generalization of the Softmax Classifier. - Shehzaad Zuzar Dhuliawala; Mrinmaya Sachan; Carl Allen (Trans. Mach. Learn. Res., 2024)