1 citations · 1 across the 5 of their papers we have counts for
5 papers
Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
Hans Jarett J. Ong, Brian Godwin S. Lim, Dominic Dayta +2
Unsupervised representation learning seeks to recover latent generative factors, yet standard methods relying on statistical independence often fail to capture causal dependencies.…
Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange
Brian Godwin Lim, Dominic Dayta, Benedict Ryan Tiu +3
The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the economet…
You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference
Dominic B. Dayta
We introduce YOASOVI, an algorithm for performing fast, self-correcting stochastic optimization for Variational Inference (VI) on large Bayesian heirarchical models. To accomplish…
Variance Control for Black Box Variational Inference Using The James-Stein Estimator
Dominic B. Dayta
Black Box Variational Inference is a promising framework in a succession of recent efforts to make Variational Inference more ``black box". However, in basic version it either fail…
Semiparametric Latent Topic Modeling on Consumer-Generated Corpora
Dominic B. Dayta, Erniel B. Barrios
Legacy procedures for topic modelling have generally suffered problems of overfitting and a weakness towards reconstructing sparse topic structures. With motivation from a consumer…