51 citations · 100 across the 9 of their papers we have counts for
9 papers
Taxonomy-Structured Domain Adaptation
Tianyi Liu, Zihao Xu, Hao He +3
Domain adaptation aims to mitigate distribution shifts among different domains. However, traditional formulations are mostly limited to categorical domains, greatly simplifying nua…
Graph-Relational Domain Adaptation
Zihao Xu, Hao He, Guang-He Lee +2
Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; t…
Oblique Decision Trees from Derivatives of ReLU Networks
Guang-He Lee, Tommi S. Jaakkola
We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on…
Towards Robust, Locally Linear Deep Networks
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping…
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Guang-He Lee, Yang Yuan, Shiyu Chang +1
Strong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization. Specifically, an bounded adversary cannot alter the…
Functional Transparency for Structured Data: a Game-Theoretic Approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis +1
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors t…
Game-Theoretic Interpretability for Temporal Modeling
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Interpretability has arisen as a key desideratum of machine learning models alongside performance. Approaches so far have been primarily concerned with fixed dimensional inputs emp…
MUSE: Modularizing Unsupervised Sense Embeddings
Guang-He Lee, Yun-Nung Chen
This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given c…
Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization
Guang-He Lee, Shao-Wen Yang, Shou-De Lin
The objective function of a matrix factorization model usually aims to minimize the average of a regression error contributed by each element. However, given the existence of stoch…