96 citations · 162 across the 6 of their papers we have counts for
6 papers
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Tianlong Chen, Sijia Liu, Shiyu Chang +2
Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forget…
Optimizer Amalgamation
Tianshu Huang, Tianlong Chen, Sijia Liu +3
Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theo…
How Much Automation Does a Data Scientist Want?
Dakuo Wang, Q. Vera Liao, Yunfeng Zhang +5
Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \…
Training Stronger Baselines for Learning to Optimize
Tianlong Chen, Weiyi Zhang, Jingyang Zhou +4
Learning to optimize (L2O) has gained increasing attention since classical optimizers require laborious problem-specific design and hyperparameter tuning. However, there is a gap b…
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Tianlong Chen, Sijia Liu, Shiyu Chang +3
Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy. However, gaining robustness from pretraining is left une…
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen +3
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper add…