See my Google Scholar page for a full list and citation counts.

In chronological order:

GenIE: Generative Information Extraction

Martin Josifoski, Nicola De Cao, Maxime Peyrard, Robert West (2022). In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022)
[ link | PDF | code ]

Sparse Interventions in Language Models with Differentiable Masking

Nicola De Cao, Leon Schmid, Dieuwke Hupkes, Ivan Titov (2021). In arXiv preprint arXiv:2112.06837
[ link | PDF ]

Highly Parallel Autoregressive Entity Linking with Discriminative Correction

Nicola De Cao, Wilker Aziz, Ivan Titov (2021). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) Oral
[ link | PDF | code ]

Editing Factual Knowledge in Language Models

Nicola De Cao, Wilker Aziz, Ivan Titov (2021). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) Oral
[ link | PDF | code ]

Multilingual Autoregressive Entity Linking

Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni (2022). In Transactions of the Association for Computational Linguistics (TACL)
[ link | PDF | code ]

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih (2021). In arXiv preprint arXiv:2101.00133
[ link | PDF ]

A Memory Efficient Baseline for Open Domain Question Answering

Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Sebastian Riedel, Edouard Grave (2020). In arXiv preprint arXiv:2012.15156
[ link | PDF ]

Autoregressive Entity Retrieval

Nicola De Cao, Gautier Izacard, Fabio Petroni, Sebastian Riedel (2021). In Proceedings of the 9th International Conference on Learning Representations (ICLR) Spotlight (top 5%)
[ link | PDF | code ]

Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking

Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov (2021). In Proceedings of the 9th International Conference on Learning Representations (ICLR) Spotlight (top 5%)
[ link | PDF | code ]

KILT: a Benchmark for Knowledge Intensive Language Tasks

Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vassilis Plachouras, Tim Rocktäschel, Sebastian Riedel (2021). In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021).
[ link | PDF | code ]

The Power Spherical distribution

Nicola De Cao, Wilker Aziz (2020). In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), INNF+.
[ link | PDF | code ]

How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, Ivan Titov (2020). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020).
[ link | PDF | code ]

Block Neural Autoregressive Flow

Nicola De Cao, Wilker Aziz, Ivan Titov (2019). In 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019).
[ link | PDF | code | poster ]

Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Nicola De Cao, Wilker Aziz, Ivan Titov (2019). In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019).
[ link | PDF | poster | poster (portrait) ]

Explorations in Homeomorphic Variational Auto-Encoding

Luca Falorsi*, Pim de Haan*, Tim R. Davidson*, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen (2018). In ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models.
*equal contribution.
[ link | PDF | code | blog ]

MolGAN: An implicit generative model for small molecular graphs

Nicola De Cao, Thomas Kipf (2018). In ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models.
[ link | PDF | code ]

Hyperspherical Variational Auto-Encoders

Tim R. Davidson*, Luca Falorsi*, Nicola De Cao*, Thomas Kipf, Jakub M. Tomczak (2018). In 34th Conference on Uncertainty in Artificial Intelligence (UAI 2018). Spotlight
*equal contribution.
[ link | PDF | code | blog ]

Deep Generative Models for Graphs: VAEs, GANs, and reinforcement learning for de novo drug discovery

Nicola De Cao, supervised by Thomas Kipf and Max Welling (2018). Master Thesis
Published at in ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models as MolGAN: An implicit generative model for small molecular graphs.
[ PDF | slides | code ]