Supervised learning models are challenged by the intrinsic complexities ...
Many machine learning tasks can be formulated as a stochastic compositio...
Recently, significant progress has been made in understanding the
genera...
Recently, there has been an increasing adoption of differential privacy
...
Recently, contrastive learning has found impressive success in advancing...
Recently there is a large amount of work devoted to the study of Markov ...
In this paper, by introducing a low-noise condition, we study privacy an...
The use of machine learning models in consequential decision making ofte...
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessi...
Stochastic gradient descent ascent (SGDA) and its variants have been the...
Pairwise learning refers to learning tasks where the loss function depen...
Recently, model-agnostic meta-learning (MAML) has garnered tremendous
at...
In forming learning objectives, one oftentimes needs to aggregate a set ...
Many machine learning problems can be formulated as minimax problems suc...
eep UC (area under the ROC curve)
aximization (DAM) has attracted much a...
In this paper, we are concerned with differentially private SGD algorith...
In this paper, we aim to develop stochastic hard thresholding algorithms...
In forming learning objectives, one oftentimes needs to aggregate a set ...
The Area Under the ROC Curve (AUC) is a widely used performance measure ...
Recently there are a considerable amount of work devoted to the study of...
Stochastic AUC maximization has garnered an increasing interest due to b...
In this paper we consider the problem of maximizing the Area under the R...
Online learning algorithms update models via one sample per iteration, t...
Stochastic optimization algorithms update models with cheap per-iteratio...
In this paper we study the stability and its trade-off with optimization...
In recent years, correntropy and its applications in machine learning ha...
In recent years, correntropy and its applications in machine learning ha...
In this work, we introduce the average top-k (AT_k) loss as a new
ensemb...
In this paper, we consider unregularized online learning algorithms in a...
Pairwise learning usually refers to a learning task which involves a los...
Recently, metric learning and similarity learning have attracted a large...