Pitfalls of in-domain uncertainty estimation and ensembling in deep learning A Ashukha, A Lyzhov, D Molchanov, D Vetrov arXiv preprint arXiv:2002.06470, 2020 | 316 | 2020 |
Greedy policy search: A simple baseline for learnable test-time augmentation A Lyzhov, Y Molchanova, A Ashukha, D Molchanov, D Vetrov Conference on uncertainty in artificial intelligence, 1308-1317, 2020 | 56 | 2020 |
Inverse Scaling: When Bigger Isn't Better IR McKenzie, A Lyzhov, M Pieler, A Parrish, A Mueller, A Prabhu, ... arXiv preprint arXiv:2306.09479, 2023 | 35 | 2023 |
Inverse scaling prize: Second round winners I McKenzie, A Lyzhov, A Parrish, A Prabhu, A Mueller, N Kim, S Bowman, ... Fund for Alignment Research (FAR), 2022 | 11 | 2022 |
The inverse scaling prize, 2022 I McKenzie, A Lyzhov, A Parrish, A Prabhu, A Mueller, N Kim, S Bowman, ... URL https://github. com/inverse-scaling/prize, 2022 | 11 | 2022 |
Normative disagreement as a challenge for cooperative AI J Stastny, M Riché, A Lyzhov, J Treutlein, A Dafoe, J Clifton arXiv preprint arXiv:2111.13872, 2021 | 9 | 2021 |
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning. arXiv A Ashukha, A Lyzhov, D Molchanov, D Vetrov arXiv preprint arXiv:2002.06470, 2020 | 5 | 2020 |
Cross-Pollinated Deep Ensembles A Lyzhov, D Voronkova, D Vetrov NeurIPS Europe meetup on Bayesian Deep Learning, 2020 | | 2020 |