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Found 14 papers, 2 papers with code
Optimal Insurance to Maximize Exponential Utility when Premium is Computed by a Convex Functional
We find the optimal indemnity to maximize the expected utility of terminal wealth of a buyer of insurance whose preferences are modeled by an exponential utility.
Optimal moral-hazard-free reinsurance under extended distortion premium principles
Due to the presence of the incentive compatibility constraint and the nonconcavity of the distortion, the optimal contract is obtained as a solution to a double obstacle problem.
Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models
We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences.
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.
Mean-Variance Portfolio Selection in Contagious Markets
We consider a mean-variance portfolio selection problem in a financial market with contagion risk.
Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process
By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form.
Hedging with Bitcoin Futures: The Effect of Liquidation Loss Aversion and Aggressive Trading
We consider the hedging problem where a futures position can be automatically liquidated by the exchange without notice.
A Perturbation Approach to Optimal Investment, Liability Ratio, and Dividend Strategies
We study an optimal dividend problem for an insurer who simultaneously controls investment weights in a financial market, liability ratio in the insurance business, and dividend payout rate.
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images
To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time.
MNN: A Universal and Efficient Inference Engine
Deploying deep learning models on mobile devices draws more and more attention recently.
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification
By DAS, the weights parameters and architecture parameters (corresponds to the hyperparameters but not the topologies) can be optimized by stochastic gradient descent method during the training.
Optimal Bookmaking
We introduce a general framework for continuous-time betting markets, in which a bookmaker can dynamically control the prices of bets on outcomes of random events.
Band Attention Convolutional Networks For Hyperspectral Image Classification
Unlike most of deep learning methods used in HSIs, the band attention module which is customized according to the characteristics of hyperspectral images is embedded in the ordinary CNNs for better performance.
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework
To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form.
