activity
20122024
most citedCounterfactual Fairness

841 citations · 1.8k across the 37 of their papers we have counts for

collaborators

40 papers

cs.LG2024

Proxy Methods for Domain Adaptation

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen +5

We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the…

cs.LG2024

Setting the Record Straight on Transformer Oversmoothing

Gbètondji J-S Dovonon, Michael M. Bronstein, Matt J. Kusner

Transformer-based models have recently become wildly successful across a diverse set of domains. At the same time, recent work has shown empirically and theoretically that Transfor…

cs.LG2023★ 8 cited

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

Jean Kaddour, Oscar Key, Piotr Nawrot +2

The computation necessary for training Transformer-based language models has skyrocketed in recent years. This trend has motivated research on efficient training algorithms designe…

cs.LG2023

DAG Learning on the Permutahedron

Valentina Zantedeschi, Luca Franceschi, Jean Kaddour +2

We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutatio…

stat.ML2022★ 3 cited

Adapting to Latent Subgroup Shifts via Concepts and Proxies

Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour +7

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When thi…

cs.LG2022★ 2 cited

Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach

Yuchen Zhu, Limor Gultchin, Arthur Gretton +2

We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setti…