3 citations · 5 across the 10 of their papers we have counts for
11 papers
Task-Aware Multi-Expert Architecture For Lifelong Deep Learning
Jianyu Wang, Jacob Nean-Hua Sheikh, Cat P. Le +1
Lifelong deep learning (LDL) trains neural networks to learn sequentially across tasks while preserving prior knowledge. We propose Task-Aware Multi-Expert (TAME), a continual lear…
CATE Estimation With Potential Outcome Imputation From Local Regression
Ahmed Aloui, Juncheng Dong, Cat P. Le +1
One of the most significant challenges in Conditional Average Treatment Effect (CATE) estimation is the statistical discrepancy between distinct treatment groups. To address this i…
Perceiver-based CDF Modeling for Time Series Forecasting
Cat P. Le, Chris Cannella, Ali Hasan +2
Transformers have demonstrated remarkable efficacy in forecasting time series data. However, their extensive dependence on self-attention mechanisms demands significant computation…
Mode-Aware Continual Learning for Conditional Generative Adversarial Networks
Cat P. Le, Juncheng Dong, Ahmed Aloui +1
The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end,…
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model
Cat P. Le, Luke Dai, Michael Johnston +3
Effective evaluation methods remain a significant challenge for research on open-domain conversational dialogue systems. Explicit satisfaction ratings can be elicited from users, b…
Transfer Learning for Individual Treatment Effect Estimation
Ahmed Aloui, Juncheng Dong, Cat P. Le +1
This work considers the problem of transferring causal knowledge between tasks for Individual Treatment Effect (ITE) estimation. To this end, we theoretically assess the feasibilit…
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
Cat P. Le, Juncheng Dong, Mohammadreza Soltani +1
We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartit…
Fisher Task Distance and Its Application in Neural Architecture Search
Cat P. Le, Mohammadreza Soltani, Juncheng Dong +1
We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices, called Fisher task distance. This distance represents the complexity of…
Improved Automated Machine Learning from Transfer Learning
Cat P. Le, Mohammadreza Soltani, Robert Ravier +1
In this paper, we propose a neural architecture search framework based on a similarity measure between some baseline tasks and a target task. We first define the notion of the task…
Task-Aware Neural Architecture Search
Cat P. Le, Mohammadreza Soltani, Robert Ravier +1
The design of handcrafted neural networks requires a lot of time and resources. Recent techniques in Neural Architecture Search (NAS) have proven to be competitive or better than t…
Supervised Encoding for Discrete Representation Learning
Cat P. Le, Yi Zhou, Jie Ding +1
Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels. Such a learning framework ty…