Artificial Intelligence
Sixteen papers authored by members of the Department of Computing have been accepted at the 11th International Conference on Learning Representations (ICLR 2023), the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), and the 40th International Conference on Machine Learning (ICML 2023).
Here is the full list:
ICLR 2023:
- W. Chen and Y. Li. Calibrating Transformers via Sparse Gaussian Processes.
- M. Monteiro, F. De Sousa Ribeiro, N. Pawlowski, D. Castro, B. Glocker. Measuring axiomatic soundness of counterfactual image models.
- A. Creswell, M. Shanahan, I. Higgins; Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.
- F. Chalumeau, R. Boige, B. Lim, V. Macé, M. Allard, A. Flajolet, T. Pierrot, A. Cully; Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery.
IJCAI 2023:
- S. Huang, P. Chen, J. McCann; DiffAR: Adaptive Conditional Diffusion Model for Temporal-augmented Human Activity Recognition.
- D. Cunnington, M. Law, J. Lobo, A. Russo; Neuro-Symbolic Learning of Answer Set Programs from Raw Data.
- A. Hussain, F. Belardinelli, G. Piliouras; Beyond Strict Competition: Approximate Convergence of Multi Agent Q-Learning Dynamics.
- F. Belardinelli, A. Ferrando, W. Jamroga, V. Malvone, A. Murano; Scalable Veri?cation of Strategy Logic by Three-Valued Abstraction.
ICML 2023:
- A. Hussain, F. Belardinelli, D. Paccagnan; The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics.
- H. Zhu, C. Balsells Rodas and Y. Li; Markovian Gaussian Process Variational Auto-Encoders.
- F. De Sousa Ribeiro, T. Xia, M. Monteiro, N. Pawlowski, B. Glocker; High Fidelity Image Counterfactuals with Probabilistic Causal Models.
- A. Immer, T. van der Ouderaa, M. van der Wilk, G. Ratsch, B. Schölkopf; Stochastic Marginal Likelihood Gradients
using Neural Tangent Kernels.
- D. Zhang, C. Rainone, M. Peschl, R. Bondesan; Robust scheduling with GFlowNets.
- D. Furelos-Blanco, M. Law, A. Jonsson, K. Broda, A. Russo; Hierarchies of Reward Machines.
- E. Pearce-Crump; Brauer's Group Equivariant Neural Networks.
- E. Pearce-Crump; How Jellyfish Characterise Alternating Group Equivariant Neural Networks.
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Reporter
Mr Ahmed Idle
Department of Computing
