Results 71 to 80 of about 9,788 (160)
Financial Review, Volume 60, Issue 4, Page 1483-1503, November 2025.ABSTRACT We study the target return strategy (TRS), which exits the market once the return reaches a preset target. We show that the holding‐period return (HPR) cannot mean‐variance dominate TRS, but TRS can mean‐variance dominate HPR. We theoretically analyze TRS and quantitatively illustrate that training targets by a mean‐variance utility ...
Ying Xue, Zheng Wen, Xu Jiang
wiley +1 more source
Convex Analysis and Optimization with Submodular Functions: a Tutorial [PDF]
, 2010 Set-functions appear in many areas of computer science and applied mathematics, such as machine learning, computer vision, operations research or electrical networks.
Bach, Francis
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Scalable Submodular Policy Optimization via Pruned Submodularity Graph
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Anand, Aditi +2 more
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Differentially Private Online Submodular Optimization
, 2018 In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. A sequence of $T$ submodular functions over a collection of $n$ elements arrive online, and at each timestep the algorithm must choose a subset of $[n]$ before seeing the function.
Cardoso, Adrian Rivera, Cummings, Rachel
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Advanced Intelligent Systems, Volume 7, Issue 10, October 2025.Image classification plays a pivotal role in biomedical image analysis. Herein, it is shown that large multimodal models, such as GPT‐4, achieve superior performance in one‐shot learning, generalization, interpretability, and text‐driven image classification. Applications span tissue, cell type, cellular state, and disease classification, outperforming
Wenpin Hou +4 more
wiley +1 more source
Queue-Aware Cell Activation and User Association for Traffic Offloading via Dual-Connectivity
IEEE Access, 2019 With the objective of reducing energy cost, we study the stochastic optimization of traffic off-loading via dual-connectivity by joint cell activation and user association.
Qiaoni Han, Bo Yang, Xiaocheng Wang
doaj +1 more source
Optimization of Chance-Constrained Submodular Functions
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints.
Doerr, Benjamin +4 more
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Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors
IEEE Access, 2019 Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs.
Junbin Liang +3 more
doaj +1 more source
Non‐Stationary Search and Assortative Matching
Econometrica, Volume 93, Issue 5, Page 1635-1662, September 2025.This paper studies assortative matching in a non‐stationary search‐and‐matching model with non‐transferable payoffs. Non‐stationarity entails that the number and characteristics of agents searching evolve endogenously over time. Assortative matching can fail in non‐stationary environments under conditions for which Morgan (1995) and Smith (2006) show ...
Nicolas Bonneton, Christopher Sandmann
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Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
, 2019 Submodular maximization is a general optimization problem with a wide range of applications in machine learning (e.g., active learning, clustering, and feature selection). In large-scale optimization, the parallel running time of an algorithm is governed
Fahrbach, Matthew +2 more
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