163 citations · 1.2k across the 128 of their papers we have counts for
12 papers · 1 filter
Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Rong Jin +2
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple mod…
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Zhuoning Yuan, Yan Yan, Milan Sonka +1
Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focu…
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…
Adam: A Stochastic Method with Adaptive Variance Reduction
Mingrui Liu, Wei Zhang, Francesco Orabona +1
Adam is a widely used stochastic optimization method for deep learning applications. While practitioners prefer Adam because it requires less parameter tuning, its use is problemat…
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh +3
Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators…
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
Yan Yan, Xin Man, Tianbao Yang
In this paper, we propose robust stochastic algorithms for solving convex compositional problems of the form $f(\E_ξg(\cdot; ξ)) + r(\cdot)$ by establishing {\bf sub-Gaussian confi…