Data-Driven Subgroup Identification for Linear Regression

1 code implementation • 29 Apr 2023 • Zachary Izzo, Ruishan Liu, James Zou

To do this, simple parametric models are frequently used (e. g. coefficients of linear regression) but usually fitted on the whole dataset.

regression

Learning transport cost from subset correspondence

no code implementations • ICLR 2020 • Ruishan Liu, Akshay Balsubramani, James Zou

Optimal transport (OT) is a principled approach to align datasets, but a key challenge in applying OT is that we need to specify a transport cost function that accurately captures how the two datasets are related.

Model Compression with Generative Adversarial Networks

no code implementations • ICLR 2019 • Ruishan Liu, Nicolo Fusi, Lester Mackey

Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as deep neural networks and large random forests on both image and tabular datasets.

Generative Adversarial Network Image Classification +1

Teacher-Student Compression with Generative Adversarial Networks

1 code implementation • ICLR 2019 • Ruishan Liu, Nicolo Fusi, Lester Mackey

Our GAN-assisted TSC (GAN-TSC) significantly improves student accuracy for expensive models such as large random forests and deep neural networks on both tabular and image datasets.

Generative Adversarial Network Image Classification +1

The Effects of Memory Replay in Reinforcement Learning

1 code implementation • 18 Oct 2017 • Ruishan Liu, James Zou

We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance.

Q-Learning reinforcement-learning +1