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  1. Model selection is a strategy aimed at creating accurate and robust models by identifying the optimal model for classifying any particular input sample. This paper proposes a novel framework for differentiable selection of groups of models by integrating machine learning and combinatorial optimization.The framework is tailored for ensemble learning with a strategy that learns to combine the predictions of appropriately selected pre-trained ensemble models. It does so by modeling the ensemble learning task as a differentiable selection program trained end-to-end over a pretrained ensemble to optimize task performance. The proposed framework demonstrates its versatility and effectiveness, outperforming conventional and advanced consensus rules across a variety of classification tasks.

     
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    Free, publicly-accessible full text available August 1, 2024
  2. AC Optimal Power Flow (AC-OPF) is a fundamental building block in power system optimization. It is often solved repeatedly, especially in regions with large penetration of renewable generation, to avoid violating operational limits. Recent work has shown that deep learning can be effective in providing highly accurate approximations of AC-OPF. However, deep learning approaches may suffer from scalability issues, especially when applied to large realistic grids. This paper addresses these scalability limitations and proposes a load embedding scheme using a 3-step approach. The first step formulates the load embedding problem as a bilevel optimization model that can be solved using a penalty method. The second step learns the encoding optimization to quickly produce load embeddings for new OPF instances. The third step is a deep learning model that uses load embeddings to produce accurate AC-OPF approximations. The approach is evaluated experimentally on large-scale test cases from the NESTA library. The results demonstrate that the proposed approach produces an order of magnitude improvements in training convergence and prediction accuracy. 
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  3. Learning mappings between system loading and optimal dispatch solutions has been a recent topic of interest in the power systems and machine learning communities. However, previous works have ignored practical power system constraints such as generator ramp limits and other intertemporal requirements. Additionally, optimal power flow runs are not performed independently of previous timesteps - in most cases, an OPF solution representing the current state of the system is heavily related to the OPF solution from previous timesteps. In this paper, we train a recurrent neural network, which embeds natural relationships between timesteps, to predict the optimal solution of convex power systems optimization problems with intertemporal constraints. In contrast to traditional forecasting methods, the computational benefits from this technique can allow operators to rapidly simulate forecasts of system operation and corresponding optimal solutions to provide a more comprehensive view of future system states. 
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  4. This paper presents a conceptual review of our recent advancements on the integration of machine learning and optimization. It focuses on describing new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. 
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  5. The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the predicted rankings. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints, while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve on current state-of-the-art fair learning-to-rank systems with respect to established performance metrics. 
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  6. The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes. It models the optimal scheduling of multiple sequences of tasks, each under a fixed order of operations, in which individual tasks require exclusive access to a predetermined resource for a specified processing time. The problem is NP-hard and computationally challenging even for medium-sized instances. Motivated by the increased stochasticity in production chains, this paper explores a deep learning approach to deliver efficient and accurate approximations to the JSP. In particular, this paper proposes the design of a deep neural network architecture to exploit the problem structure, its integration with Lagrangian duality to capture the problem constraints, and a post-processing optimization to guarantee solution feasibility.The resulting method, called JSP-DNN, is evaluated on hard JSP instances from the JSPLIB benchmark library. Computational results show that JSP-DNN can produce JSP approximations of high quality at negligible computational costs. 
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