no code implementations • 4 Dec 2023 • Bokun Wang, Tianbao Yang
This paper revisits a class of convex Finite-Sum Coupled Compositional Stochastic Optimization (cFCCO) problems with many applications, including group distributionally robust optimization (GDRO), learning with imbalanced data, reinforcement learning, and learning to rank.
no code implementations • 29 Aug 2023 • Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services.
1 code implementation • 14 May 2023 • Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e. g., multiple 2D slices of a CT scan for a patient).
1 code implementation • 14 Jun 2022 • Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji
GraphFM-IB applies FM to in-batch sampled data, while GraphFM-OB applies FM to out-of-batch data that are 1-hop neighborhood of in-batch data.
no code implementations • 1 Mar 2022 • Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang
In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning.
no code implementations • 24 Feb 2022 • Bokun Wang, Tianbao Yang
This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index.
no code implementations • 15 Feb 2022 • Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
To address these limitations, we propose a Stochastic Multi-level Variance Reduction method (SMVR), which achieves the optimal sample complexity of $\mathcal{O}\left(1 / \epsilon^{3}\right)$ to find an $\epsilon$-stationary point for non-convex objectives.
1 code implementation • 9 Jun 2021 • Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
The proposed algorithms require sampling a constant number of tasks and data samples per iteration, making them suitable for the continual learning scenario.
no code implementations • 7 Jun 2021 • Bokun Wang, Mher Safaryan, Peter Richtárik
To address the high communication costs of distributed machine learning, a large body of work has been devoted in recent years to designing various compression strategies, such as sparsification and quantization, and optimization algorithms capable of using them.
1 code implementation • ICLR 2022 • Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
We propose a family of adaptive integer compression operators for distributed Stochastic Gradient Descent (SGD) that do not communicate a single float.
no code implementations • 3 May 2020 • Bokun Wang, Shiqian Ma, Lingzhou Xue
However, most of the existing Riemannian stochastic algorithms require the objective function to be differentiable, and they do not apply to the case where the objective function is nonsmooth.
no code implementations • 29 Jan 2019 • Bokun Wang, Ian Davidson
Fair clustering under the disparate impact doctrine requires that population of each protected group should be approximately equal in every cluster.