no code implementations • 17 Feb 2023 • Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris, Michael L. Salinas, Robert S. Chapkin, Bani K. Mallick
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a directed acyclic graph.
no code implementations • 18 Sep 2022 • Yang Ni
Causal discovery for quantitative data has been extensively studied but less is known for categorical data.
no code implementations • 1 Aug 2022 • Sina Shahhosseini, Yang Ni, Hamidreza Alikhani, Emad Kasaeyan Naeini, Mohsen Imani, Nikil Dutt, Amir M. Rahmani
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
no code implementations • 14 May 2022 • Yang Ni, Danny Abraham, Mariam Issa, Yeseong Kim, Pietro Mercati, Mohsen Imani
QHD provides real-time learning by further decreasing the memory capacity and the batch size.
no code implementations • 19 Jan 2022 • Yang Ni, Bani Mallick
Causal discovery for purely observational, categorical data is a long-standing challenging problem.
no code implementations • NeurIPS 2020 • Junsouk Choi, Robert Chapkin, Yang Ni
To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.
no code implementations • 28 Jun 2019 • Yang Ni, Yuan Ji, Peter Mueller
Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model.
no code implementations • 28 May 2019 • Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.