559 citations · 914 across the 14 of their papers we have counts for
15 papers
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Vijay Ekambaram, Arindam Jati, Nam Nguyen +2
Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. However, their high memory and computing requirements pose a…
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong +1
We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components:…
Interpretable Clustering via Multi-Polytope Machines
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen +2
Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use…
Deep Policy Iteration with Integer Programming for Inventory Management
Pavithra Harsha, Ashish Jagmohan, Jayant Kalagnanam +2
We present a Reinforcement Learning (RL) based framework for optimizing long-term discounted reward problems with large combinatorial action space and state dependent constraints.…
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Haoran Zhu, Pavankumar Murali, Dzung T. Phan +2
Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a gr…
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun +2
We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly l…
DTN: A Learning Rate Scheme with Convergence Rate of for SGD
Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan +2
This paper has some inconsistent results, i.e., we made some failed claims because we did some mistakes for using the test criterion for a series. Precisely, our claims on the conv…
Finite-Sum Smooth Optimization with SARAH
Lam M. Nguyen, Marten van Dijk, Dzung T. Phan +3
The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finit…
When Does Stochastic Gradient Algorithm Work Well?
Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan +2
In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification. We consider…
Development of hp-inverse model by using generalized polynomial chaos
Kyongmin Yeo, Youngdeok Hwang, Xiao Liu +1
We present a hp-inverse model to estimate a smooth, non-negative source function from a limited number of observations for a two-dimensional linear source inversion problem. A stan…
Optimal Generalized Decision Trees via Integer Programming
Oktay Gunluk, Jayant Kalagnanam, Minhan Li +2
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not alw…
A Spatio-Temporal Modeling Approach for Weather Radar Reflectivity Data and Its Applications in Tropical Southeast Asia
Xiao Liu, Viknesswaran Gopal, Jayant Kalagnanam
Weather radar echoes, correlated in both space and time, are the most important input data for short-term precipitation forecast. Motivated by real datasets, this paper is concerne…
Statistical Modeling for Spatio-Temporal Degradation Data
Xiao Liu, Kyongmin Yeo, Jayant Kalagnanam
This paper investigates the modeling of an important class of degradation data, which are collected from a spatial domain over time; for example, the surface quality degradation. L…
A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis
Jayant Kalagnanam, Max Henrion
There has long been debate about the relative merits of decision theoretic methods and heuristic rule-based approaches for reasoning under uncertainty. We report an experimental co…