1 code implementation • NAACL 2021 • Qi Liu, Matt Kusner, Phil Blunsom
We propose a data augmentation method for neural machine translation.
1 code implementation • 22 Feb 2022 • Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus
Causal effect estimation is important for many tasks in the natural and social sciences.
no code implementations • 28 Sep 2020 • Valentina Zantedeschi, Matt Kusner, Vlad Niculae
In this work we derive a novel sparse relaxation for binary tree learning.
no code implementations • 28 Sep 2020 • Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt Kusner
There has recently been a flurry of exciting advances in deep learning models on point clouds.
no code implementations • 18 Jun 2022 • Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt Kusner, Ricardo Silva
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error.
no code implementations • 10 Nov 2023 • Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion.