Search Results for author: Yang Ni

Found 8 papers, 0 papers with code

Graphical Dirichlet Process

no code implementations17 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.

Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

no code implementations18 Sep 2022 Yang Ni

Causal discovery for quantitative data has been extensively studied but less is known for categorical data.

Causal Discovery

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

no code implementations1 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.

BIG-bench Machine Learning Privacy Preserving

Ordinal Causal Discovery

no code implementations19 Jan 2022 Yang Ni, Bani Mallick

Causal discovery for purely observational, categorical data is a long-standing challenging problem.

Causal Discovery

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

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.

Bayesian Inference

Consensus Monte Carlo for Random Subsets using Shared Anchors

no code implementations28 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.

Adversarial Domain Adaptation Being Aware of Class Relationships

no code implementations28 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.

Domain Adaptation Transfer Learning

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