no code implementations • 18 Apr 2023 • Yuwei Yin, Jean Kaddour, Xiang Zhang, Yixin Nie, Zhenguang Liu, Lingpeng Kong, Qi Liu
In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
no code implementations • 17 Apr 2023 • Jean Kaddour
MiniPile is a 6GB subset of the deduplicated 825GB The Pile corpus.
1 code implementation • 9 Mar 2023 • Aengus Lynch, Gbètondji J-S Dovonon, Jean Kaddour, Ricardo Silva
The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.
1 code implementation • 27 Jan 2023 • Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data.
1 code implementation • 29 Sep 2022 • Jean Kaddour
Training vision or language models on large datasets can take days, if not weeks.
no code implementations • 30 Jun 2022 • Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
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 • 1 Feb 2022 • Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.
2 code implementations • NeurIPS 2021 • Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).
1 code implementation • NeurIPS 2020 • Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth
However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner?