52 citations · 55 across the 7 of their papers we have counts for
7 papers
Learning Collapsed Patterns in Compositional Data: A Bayesian Heterogeneous Relative-Shift Approach
Maoran Xu, Guanyu Hu
Relative-shift regression provides a principled framework for modeling compositional covariates by quantifying how the response changes when mass is reallocated from one component…
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
Zi-Hao Qiu, Siqi Guo, Mao Xu +3
The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particula…
Identifiable and interpretable nonparametric factor analysis
Maoran Xu, Steven Winter, Amy H. Herring +1
Factor models are widely used to reduce dimensionality in modeling high-dimensional data. However, there remains a need for models that can be reliably fit in modest sample sizes a…
Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality
Maoran Xu, Hua Zhou, Yujie Hu +1
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recov…
POP909: A Pop-song Dataset for Music Arrangement Generation
Ziyu Wang, Ke Chen, Junyan Jiang +5
Music arrangement generation is a subtask of automatic music generation, which involves reconstructing and re-conceptualizing a piece with new compositional techniques. Such a gene…
Bayesian Inference with the l1-ball Prior: Solving Combinatorial Problems with Exact Zeros
Maoran Xu, Leo L. Duan
The l1-regularization is very popular in high dimensional statistics -- it changes a combinatorial problem of choosing which subset of the parameter are zero, into a simple continu…
Bayesian Multi-scale Modeling of Factor Matrix without using Partition Tree
Maoran Xu, Leo L. Duan
The multi-scale factor models are particularly appealing for analyzing matrix- or tensor-valued data, due to their adaptiveness to local geometry and intuitive interpretation. Howe…