57 citations · 137 across the 12 of their papers we have counts for
12 papers
Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization
Beomjun Kim, Jeongho Kim, Subin Huh +2
In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride req…
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Doyup Lee, Suehun Jung, Yeongjae Cheon +2
Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to co…
Signal-Anticipation in Local Voltage Control in Distribution Systems
Zhiyuan Liu, Seungil You, Xinyang Zhou +2
We consider the signal-anticipating behavior in local Volt/Var control for distribution systems. Such a behavior makes interaction among the nodes a game. We characterize Nash equi…
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter, Heinrich Jiang, Serena Wang +4
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study th…
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang +5
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the gene…
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Serena Wang, Maya Gupta, Seungil You
Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensembl…
Deep Lattice Networks and Partial Monotonic Functions
Seungil You, David Ding, Kevin Canini +2
We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators…
Dual Link Algorithm for the Weighted Sum Rate Maximization in MIMO Interference Channels
Xing Li, Seungil You, Lijun Chen +3
MIMO interference network optimization is important for increasingly crowded wireless communication networks. We provide a new algorithm, named Dual Link algorithm, for the classic…
A Convex Approach to Sparse H infinity Analysis & Synthesis
Seungil You, Nikolai Matni
In this paper, we propose a new robust analysis tool motivated by large-scale systems. The H infinity norm of a system measures its robustness by quantifying the worst-case behavio…
Primal robustness and semidefinite cones
Seungil You, Ather Gattami, John C. Doyle
This paper reformulates and streamlines the core tools of robust stability and performance for LTI systems using now-standard methods in convex optimization. In particular, robustn…
H infinity Analysis Revisited
Seungil You, Ather Gattami
This paper proposes a direct, and simple approach to the H infinity norm calculation in more general settings. In contrast to the method based on the Kalman-Yakubovich-Popov lemma,…
The Weighted Sum Rate Maximization in MIMO Interference Networks: The Minimax Lagrangian Duality and Algorithm
Lijun Chen, Seungil You
We take a new perspective on the weighted sum-rate maximization in multiple-input multiple-output (MIMO) interference networks, by formulating an equivalent max-min problem. This s…