5 citations · 9 across the 5 of their papers we have counts for
5 papers
Syndrome decoding meets multiple instances
Haoxuan Wu, Jincheng Zhuang
The NP-hard problem of decoding random linear codes is crucial to both coding theory and cryptography. In particular, this problem underpins the security of many code based post-qu…
Interpretable Latent Variables in Deep State Space Models
Haoxuan Wu, David S. Matteson, Martin T. Wells
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data. The model estimate…
Drift vs Shift: Decoupling Trends and Changepoint Analysis
Haoxuan Wu, Toryn L. J. Schafer, Sean Ryan +1
We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayes…
A News-based Machine Learning Model for Adaptive Asset Pricing
Liao Zhu, Haoxuan Wu, Martin T. Wells
The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combin…
Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring
Haoxuan Wu, Toryn L. J. Schafer, David S. Matteson
We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesia…