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Found 19 papers, 8 papers with code
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing
The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions.
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e. g., demand or travel times in delivery problems).
Computational Asymmetries in Robust Classification
In the context of adversarial robustness, we make three strongly related contributions.
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena.
Generalized Disparate Impact for Configurable Fairness Solutions in ML
We make two contributions in the field of AI fairness over continuous protected attributes.
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
An Implementation Approach to Rotation Programs
We study rotation programs within the standard implementation frame-work under complete information.
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes.
Contrastive Losses and Solution Caching for Predict-and-Optimize
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.
An Analysis of Regularized Approaches for Constrained Machine Learning
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy.
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness.
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets.
Combining Learning and Optimization for Transprecision Computing
The ML model learns the relation between variables precision and the output error; this information is then embedded in the MP focused on minimizing the number of bits.
Distributed, Parallel, and Cluster Computing
Lagrangian Duality for Constrained Deep Learning
In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
Boosting Combinatorial Problem Modeling with Machine Learning
The three pillars of constraint satisfaction and optimization problem solving, i. e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness.
A Visual Web Tool to Perform What-If Analysis of Optimization Approaches
In Operation Research, practical evaluation is essential to validate the efficacy of optimization approaches.
