1 code implementation • 5 Jun 2023 • Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar
Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget to identify data and train the model (the budget allocation problem), and finally the market compensates data providers according to their data contribution (revenue allocation problem).
no code implementations • 31 Oct 2022 • Katherine Tsai, Boxin Zhao, Sanmi Koyejo, Mladen Kolar
Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences.
no code implementations • 31 Jan 2022 • Boxin Zhao, Boxiang Lyu, Mladen Kolar
Stochastic gradient-based optimization methods, such as L-SVRG and its accelerated variant L-Katyusha (Kovalev et al., 2020), are widely used to train machine learning models. The theoretical and empirical performance of L-SVRG and L-Katyusha can be improved by sampling observations from a non-uniform distribution (Qian et al., 2021).
1 code implementation • 28 Dec 2021 • Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
1 code implementation • 6 May 2021 • Boxin Zhao, Percy S. Zhai, Y. Samuel Wang, Mladen Kolar
We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods.
1 code implementation • 19 Feb 2021 • Filip Hanzely, Boxin Zhao, Mladen Kolar
We investigate the optimization aspects of personalized Federated Learning (FL).
no code implementations • 11 Mar 2020 • Boxin Zhao, Y. Samuel Wang, Mladen Kolar
We first define a functional differential graph that captures the differences between two functional graphical models and formally characterize when the functional differential graph is well defined.
1 code implementation • NeurIPS 2019 • Boxin Zhao, Y. Samuel Wang, Mladen Kolar
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures.