Publications

Publications that are representative of ELIAI research

Ameer, Saadat-Yazdi; Xue, Li; Sandrine, Chausson; Vaishak, Belle; Björn, Ross; Jeff Z., Pan; Nadin, Kökciyan (2022). KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments. The 9th Workshop on Argument Mining Special Theme: Argument Mining in Real-World Applications 2022.
Chausson, S., Saadat-Yazdi, A., Li, X., Pan, J. Z., Belle, V., Kokciyan, N., & Ross, B. (Accepted/In press). A Web-based Tool for Detecting Argument Validity and Novelty. In The International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Demonstrations Track 2023 ACM.
Jung, Anna-Katharina; Ross, Björn; Stieglitz, Stefan (2020). Caution: Rumors ahead—A case study on the debunking of false information on TwitterBig Data & Society 7(2). [doi]
Ross, Björn; Jung, Anna-Katharina; Heisel, Jennifer; Stieglitz, Stefan (2018). Fake News on Social Media: The (In)Effectiveness of Warning Messages. In Proceedings of the International Conference on Information Systems (ICIS), San Francisco, December 2018.
V. Belle (2022). Tractable Probabilistic Models for Ethical AI. ICCS.
Xue, Li; Alan, Bundy (2022). An overview of the ABC Repair System for Datalog-like Theories, The 3rd International Workshop on Human-Like Computing.

Yuge Shi, Siddharth N, Brooks Paige, Philip Torr, Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models, In: Advances in Neural Information Processing Systems (NeurIPS), pp. 15692–15703, December (2019).
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami, Conditional Neural Processes, en. In: International Conference on Machine Learning, PMLR, pp. 1704–1713, (2018).
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh, Attentive Neural Processes, en. In: International Conference on Learning Representations, (2019).
Adrian Baddeley, Spatial Point Processes and Their Applications, en. In: Stochastic Geometry, Ed. by Lecture Notes in Mathematics, Berlin, Heidelberg: Springer, pp. 1–75, (2007).
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, Geoffrey E. Hinton, Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, In: Advances in Neural Information Processing Systems, Curran Associates, Inc., vol. 29, (2016).

Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy, Interpretable Goal-based Prediction and Planning for Autonomous Driving, arxiv.org/abs/2002.02277, (2021).
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong, Francisco Eiras, Mihai Dobre, John Redford, Subramanian Ramamoorthy, Stefano V. Albrecht, Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles, arxiv.org/abs/2108.02530, (2021).
Cillian Brewitt, Balint Gyevnar, Samuel Garcin, Stefano V. Albrecht, GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving, arxiv.org/abs/2103.06113, (2021).

Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen, Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation, arXiv preprint arXiv:2111.13517, (2021).
Davide Moltisanti, Frank Keller, Hakan Bilen, Laura Sevilla-Lara, Learning Action Changes by Measuring Verb-Adverb Textual Relationships, arXiv https://arxiv.org/abs/2303.15086v1 (2023). Code and Dataset available here.
Pinelopi Papalampidi, Frank Keller, Mirella Lapata, Film Trailer Generation via Task Decomposition, arXiv preprint arXiv:2111.08774, (2021).
Radina Dobreva, Frank Keller, Investigating Negation in Pre-trained Vision-and-language Models, Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, (2021).
Shreyank N Gowda, Laura Sevilla-Lara, Kiyoon Kim, Frank Keller, Marcus Rohrbach, A New Split for Evaluating True Zero-Shot Action Recognition, DAGM German Conference on Pattern Recognition, pp. 191-205, (2021).
Shreyank N Gowda, Laura Sevilla-Lara, Frank Keller, Marcus Rohrbach, CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition, arXiv preprint arXiv:2101.07042, (2021).

Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari: Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeurIPS 2022
Pasquale Minervini, Luca Franceschi, Mathias Niepert: Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models. CoRR abs/2209.04862 (2022)
Mathias Niepert, Pasquale Minervini, Luca Franceschi: Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions. NeurIPS 2021
Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, and Siva Reddy: Combining Modular Skills in Multitask Learning. arXiv e-prints (2022): arXiv-2202.

Craig Innes and Ram Ramamoorthy. Elaborating on Learned Demonstrations with Temporal Logic Specifications. In Proceedings of Robotics: Science and System XVI, 2020.
Mark Chevallier, Matthew Whyte, and Jacques D. Fleuriot. Constrained Training of Neural Networks via Theorem Proving, arXiv:2207.03880, 2022.

Yuri Burda, Harrison Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros (2019) Large-Scale Study of Curiosity-Driven Learning. International Conference on Learning Representations (ICLR)
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht (2022) Learning Representations for Reinforcement Learning with Hierarchical Forward Models. NeurIPS 2022 Workshop on Deep RL

This article was published on 2024-11-22