no code implementations • 4 Dec 2021 • Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi

Finally, to make RL algorithms more accessible for inventory management researchers, we also discuss a modular Python library developed that can be used to test the performance of RL algorithms with various supply chain structures.

no code implementations • NeurIPS 2020 • Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam

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 growing interest in addressing the inherent suboptimality of heuristic approaches such as CART.

no code implementations • 2 Mar 2020 • Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun, Duane S. Boning, Jayant R. Kalagnanam

Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.

no code implementations • 22 Jan 2019 • Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam

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 finite-sum smooth nonconvex objective function $F(w)=\frac{1}{n} \sum_{i=1}^n f_i(w)$ has been proven to be at least $\Omega(\sqrt{n}/\epsilon)$ for $n \leq \mathcal{O}(\epsilon^{-2})$ where $\epsilon$ denotes the attained accuracy $\mathbb{E}[ \|\nabla F(\tilde{w})\|^2] \leq \epsilon$ for the outputted approximation $\tilde{w}$ (Fang et al., 2018).

no code implementations • 22 Jan 2019 • Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Marten van Dijk

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.

no code implementations • 18 Jan 2018 • Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg

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.

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