1 citations · 1 across the 3 of their papers we have counts for
5 papers
Stochastic Interpolants in Hilbert Spaces
James Boran Yu, RuiKang OuYang, Julien Horwood +1
Although diffusion models have successfully extended to function-valued data, stochastic interpolants -- which offer a flexible way to bridge arbitrary distributions -- remain limi…
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
Xuexin Chen, Ruichu Cai, Zhengting Huang +5
We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests…
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen, Julien Horwood, Juyeon Heo +1
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks. In such cases, we show that line…
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
Julien Horwood, Emmanuel Noutahi
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress,…
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
Emmanuel Noutahi, Dominique Beaini, Julien Horwood +2
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative impo…