Publications (Computational Statistics and Machine Learning) | Department of Statistics

Publications

Mitra, R. et al. (2023) “Learning from data with structured missingness”, Nature Machine Intelligence, 5(1), pp. 13–23.

Ivanova, D. et al. (2023) “CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design”, in Proceedings of Machine Learning Research, pp. 14445–14464.

Campbell, A. et al. (2023) “Trans-Dimensional Generative Modeling via Jump Diffusion Models”, in. Curran Associates, pp. 42217–42257.

Xu, J. et al. (2023) “Deep Stochastic Processes via Functional Markov Transition Operators”, in Advances in Neural Information Processing Systems.

Miao, N. et al. (2023) “Learning Instance-Specific Augmentations by Capturing Local Invariances”, in Proceedings of Machine Learning Research, pp. 24720–24736.

Sharma, M. et al. (2023) “Do Bayesian Neural Networks Need To Be Fully Stochastic?”, in Proceedings of Machine Learning Research, pp. 7694–7722.

Miao, N. et al. (2023) “Learning Instance-Specific Augmentations by Capturing Local Invariances”, in Proceedings of Machine Learning Research, pp. 24720–24736.

Venkatesh, S. et al. (2023) “The genetic architecture of changes in adiposity during adulthood”, medRxiv.

He, Y. et al. (2023) “PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs”, in Proceedings of Machine Learning Research, pp. 121–1227.