12 citations · 30 across the 12 of their papers we have counts for
12 papers
LoCO: Low-rank Compositional Rotation Fine-tuning
An Nguyen, Jaesik Choi, Anh Tong
Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language processing and computer vision. While…
Refining Compositional Diffusion for Reliable Long-Horizon Planning
Kyowoon Lee, Yunhao Luo, Anh Tong +1
Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan di…
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee +5
Recent advancements in large language models (LLMs) based on transformer architectures have sparked significant interest in understanding their inner workings. In this paper, we in…
SigFormer: Signature Transformers for Deep Hedging
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee +2
Deep hedging is a promising direction in quantitative finance, incorporating models and techniques from deep learning research. While giving excellent hedging strategies, models in…
CASUAL: Conditional Support Alignment for Domain Adaptation with Label Shift
Anh T Nguyen, Lam Tran, Anh Tong +2
Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabeled on…
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Anh Tong, Toan Tran, Hung Bui +1
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitne…
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Anh Tong, Jaesik Choi
Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs). However, there exists…
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
Jiyeon Han, Kyowoon Lee, Anh Tong +1
In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting…
Discovering Latent Covariance Structures for Multiple Time Series
Anh Tong, Jaesik Choi
Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressive…
Automatic Generation of Probabilistic Programming from Time Series Data
Anh Tong, Jaesik Choi
Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages ma…
Searching for Topological Symmetry in Data Haystack
Kallol Roy, Anh Tong, Jaesik Choi
Finding interesting symmetrical topological structures in high-dimensional systems is an important problem in statistical machine learning. Limited amount of available high-dimensi…
The Automatic Statistician: A Relational Perspective
Yunseong Hwang, Anh Tong, Jaesik Choi
Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (AB…