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 • 16 Jun 2022 • Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt Kusner, Joan Lasenby, Qi Liu
Graph Self-Supervised Learning (GSSL) paves the way for learning graph embeddings without expert annotation, which is particularly impactful for molecular graphs since the number of possible molecules is enormous and labels are expensive to obtain.
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.
1 code implementation • NAACL 2021 • Qi Liu, Matt Kusner, Phil Blunsom
We propose a data augmentation method for neural machine translation.
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 • 28 Sep 2020 • Valentina Zantedeschi, Matt Kusner, Vlad Niculae
In this work we derive a novel sparse relaxation for binary tree learning.