Case study shows impact of co-optimizing energy and reserve for wind energy.
problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.
method Developed two models with and without co-optimization, calibrated with Spanish market parameters.
result Models show significant differences in energy and reserve management.
A new CO-Optimal Transport method optimizes transport maps between samples and features.
problem Optimal transport's limitations when samples are on different spaces.
method COOT: a novel OT problem that optimizes transport maps between samples and features simultaneously.
result COOT leads to performance improvements over state-of-the-art methods in domain adaptation and co-clustering.
Unbalanced COOT improves feature alignment robustly to outliers.
problem Optimal transport methods are sensitive to outliers in real-world data.
method COOT infers alignment between features and samples, unbalanced COOT adds robustness.
result Unbalanced COOT is robust to noise in real-world datasets.
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which i…
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
problem Inefficient conformal prediction intervals under heteroscedasticity and skewness.
method Co-optimization framework that learns center and radius through alternating optimization steps.
result CoCP yields consistently shorter intervals and state-of-the-art conditional coverage diagnostics.
Unified framework for higher-order network analysis.
problem Complex structure of space of networks.
method Measure-theoretic formalism, Gromov-Wasserstein distance, co-optimal transport distance.
result Unified theoretical treatment of generalized networks.
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each paramete…
New method selects variables for GP regression using sparse projection.
problem Identifying environmental factors affecting metal corrosion.
method Sparse projection of input variables, gradient descent optimization, non-convex marginal likelihood.
result Proposed method outperforms benchmarks in variable selection accuracy.
New framework optimizes forecasting and decision-making in dynamic systems.
problem Optimizing forecasting and decision-making processes in dynamic systems.
method Closed-loop framework using bilevel optimization.
result The proposed methodology yields consistently better performance than the standard open-loop approach.
Proposes a meta-learning method for robust portfolio optimization.
problem Optimizing a robust portfolio ensemble with diverse sub-portfolios.
method Uses a deep generative model with convolutional, LSTM, and dense layers to generate diverse sub-portfolios.
result The ensemble portfolio is robust and generalizes well, balancing performance and diversity.
Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millio…
RD-Agent(Q) automates quantitative finance research and development.
problem Challenges in asset return prediction due to high dimensionality and volatility.
method Data-centric multi-agent framework for automated research and development of quantitative strategies.
result Up to 2X higher annualized returns with 70% fewer factors.