A modern take on the bias-variance tradeoff in neural networks B Neal, S Mittal, A Baratin, V Tantia, M Scicluna, S Lacoste-Julien, ... arXiv preprint arXiv:1810.08591, 2018 | 214 | 2018 |
In search of robust measures of generalization GK Dziugaite, A Drouin, B Neal, N Rajkumar, E Caballero, L Wang, ... Advances in Neural Information Processing Systems 33, 11723-11733, 2020 | 93 | 2020 |
Introduction to causal inference B Neal Course Lecture Notes (draft), 2020 | 79 | 2020 |
Realcause: Realistic causal inference benchmarking B Neal, CW Huang, S Raghupathi arXiv preprint arXiv:2011.15007, 2020 | 36* | 2020 |
On the bias-variance tradeoff: Textbooks need an update B Neal arXiv preprint arXiv:1912.08286, 2019 | 26 | 2019 |
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation D Mahajan, I Mitliagkas, B Neal, V Syrgkanis arXiv preprint arXiv:2211.01939, 2022 | 12 | 2022 |
How well does your sampler really work? R Turner, B Neal UAI 2018, 2018 | 6 | 2018 |
In support of over-parametrization in deep reinforcement learning: an empirical study B Neal, I Mitliagkas ICML 2019 Workshop on Identifying and Understanding Deep Learning Phenomena, 2019 | 2 | 2019 |
Learning Generative Models with Locally Disentangled Latent Factors B Neal, A Lamb, S Ozair, D Hjelm, A Courville, Y Bengio, I Mitliagkas | | 2018 |