Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attention on openness, the definition of open-source foundation models remains ambiguous. This paper models the strategic interactions among the creator of a general-purpose model (the generalist) and the entity that fine-tunes the general-purpose model to a specialized domain or task (the specialist), in response to regulatory requirements on model openness. We present a stylized model of the regulator's choice of an open-source definition to evaluate which AI openness standards will establish appropriate economic incentives for developers. Our results characterize market equilibria -- specifically, upstream model release decisions and downstream fine-tuning efforts -- under various openness regulations and present a range of effective regulatory penalties and open-source thresholds. Overall, we find the model's baseline performance determines when increasing the regulatory penalty vs. the open-source threshold will significantly alter the generalist's release strategy. Our model provides a theoretical foundation for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies.

Graph Edit Distance (GED) offers a principled and flexible measure of graph similarity, as it quantifies the minimum cost needed to transform one graph into another with customizable edit operation costs. Despite recent learning-based efforts to approximate GED via vector space representations, existing methods struggle with adapting to varying operation costs. Furthermore, they suffer from inefficient, reactive mapping refinements due to reliance on isolated node-level distance as guidance. To address these issues, we propose GEN, a novel learning-based approach for flexible GED approximation. GEN addresses the varying costs adaptation by integrating operation costs prior to match establishment, enabling mappings to dynamically adapt to cost variations. Furthermore, GEN introduces a proactive guidance optimization strategy that captures graph-level dependencies between matches, allowing informed matching decisions in a single step without costly iterative refinements. Extensive evaluations on real-world and synthetic datasets demonstrate that GEN achieves up to 37.8% reduction in GED approximation error and 72.7% reduction in inference time compared with state-of-the-art methods, while consistently maintaining robustness under diverse cost settings and graph sizes.

This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed-logical dynamical systems. Optimization-based control of such systems with discrete and continuous decision variables entails the online solution of mixed-integer quadratic or linear programs, which suffer from the curse of dimensionality. Our approach aims at mitigating this issue by effectively decoupling the decision on the discrete variables and the decision on the continuous variables. Moreover, to mitigate the combinatorial growth in the number of possible actions due to the prediction horizon, we conceive the definition of decoupled Q-functions to make the learning problem more tractable. The use of reinforcement learning reduces the online optimization problem of the MPC controller from a mixed-integer linear (quadratic) program to a linear (quadratic) program, greatly reducing the computational time. Simulation experiments for a microgrid, based on real-world data, demonstrate that the proposed method significantly reduces the online computation time of the MPC approach and that it generates policies with small optimality gaps and high feasibility rates.

Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. While existing deterministic market clearing fails to accommodate the uncertainty, the recently proposed stochastic market clearing struggles to achieve desirable market properties. In this work, we propose a value-oriented forecasting approach, which tactically determines the RESs generation that enters the day-ahead market. With such a forecast, the existing deterministic market clearing framework can be maintained, and the day-ahead and real-time overall operation cost is reduced. At the training phase, the forecast model parameters are estimated to minimize expected day-ahead and real-time overall operation costs, instead of minimizing forecast errors in a statistical sense. Theoretically, we derive the exact form of the loss function for training the forecast model that aligns with such a goal. For market clearing modeled by linear programs, this loss function is a piecewise linear function. Additionally, we derive the analytical gradient of the loss function with respect to the forecast, which inspires an efficient training strategy. A numerical study shows our forecasts can bring significant benefits of the overall cost reduction to deterministic market clearing, compared to quality-oriented forecasting approach.

In the context of charging electric vehicles (EVs), the price-based demand response (PBDR) is becoming increasingly significant for charging load management. Such response usually encourages cost-sensitive customers to adjust their energy demand in response to changes in price for financial incentives. Thus, to model and optimize EV charging, it is important for charging station operator to model the PBDR patterns of EV customers by precisely predicting charging demands given price signals. Then the operator refers to these demands to optimize charging station power allocation policy. The standard pipeline involves offline fitting of a PBDR function based on historical EV charging records, followed by applying estimated EV demands in downstream charging station operation optimization. In this work, we propose a new decision-focused end-to-end framework for PBDR modeling that combines prediction errors and downstream optimization cost errors in the model learning stage. We evaluate the effectiveness of our method on a simulation of charging station operation with synthetic PBDR patterns of EV customers, and experimental results demonstrate that this framework can provide a more reliable prediction model for the ultimate optimization process, leading to more effective optimization solutions in terms of cost savings and charging station operation objectives with only a few training samples.

Machine unlearning is a desirable operation as models get increasingly deployed on data with unknown provenance. However, achieving exact unlearning -- obtaining a model that matches the model distribution when the data to be forgotten was never used -- is challenging or inefficient, often requiring significant retraining. In this paper, we focus on efficient unlearning methods for the task adaptation phase of a pretrained large language model (LLM). We observe that an LLM's ability to do in-context learning for task adaptation allows for efficient exact unlearning of task adaptation training data. We provide an algorithm for selecting few-shot training examples to prepend to the prompt given to an LLM (for task adaptation), ERASE, whose unlearning operation cost is independent of model and dataset size, meaning it scales to large models and datasets. We additionally compare our approach to fine-tuning approaches and discuss the trade-offs between the two approaches. This leads us to propose a new holistic measure of unlearning cost which accounts for varying inference costs, and conclude that in-context learning can often be more favourable than fine-tuning for deployments involving unlearning requests.

Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering, after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation. Our work vastly improves the previous $O(n^{5/6}poly\log(n))$ fair approximation for cost to a near polylogarithmic $O(n^\delta poly\log(n))$ fair approximation for any constant $\delta\in(0,1)$. This result establishes a cost-fairness tradeoff and extends to broader fairness constraints than the previous work. We also show how to alter existing hierarchical clusterings to guarantee fairness and cluster balance across any level in the hierarchy.

A customer-centric approach, real-time data integration, cost optimization, and advanced security are those topmost needs of the present times financial sector which is emulating the business dynamics of FinTech companies. The word "FinTech' is synonymous with innovations, convenience, and high accessibility in the world of finance. Equipped with Artificial Intelligence (AI), the principal purpose of the majority FinTechs is financial inclusion – enabling the masses to get benefits from the mainstream financial system. With the advent of AI in the financial sector, hindrances and complexities that people used to experience in availing the financial services of banks and NBFCs do not exist today. Thanks to the new breed of tech-oriented financial institutions and AI technology that have empowered the masses with easy and cost-effective financial solutions.

In terms of the operation of microgrids, optimal scheduling is a vital issue that must be taken into account. In this regard, this paper proposes an effective framework for optimal scheduling of renewable microgrids considering energy storage devices, wind turbines, micro turbines. Due to the nonlinearity and complexity of operation problems in microgrids, it is vital to use an accurate and robust optimization technique to efficiently solve this problem. To this end, in the proposed framework, the teacher learning-based optimization is utilized to efficiently solve the scheduling problem in the system. Moreover, a deep learning model based on bidirectional long short-term memory is proposed to address the short-term wind power forecasting problem. The feasibility and performance of the proposed framework as well as the effect of wind power forecasting on the operation efficiency are examined using IEEE 33-bus test system. Also, the Australian Wool north wind site data is utilized as a real-world dataset to evaluate the performance of the forecasting model. Results show the effective and efficient performance of the proposed framework in the optimal scheduling of microgrids.

Evacuation shelters, which are urgently required during natural disasters, are designed to minimize the burden of evacuation on human survivors. However, the larger the scale of the disaster, the more costly it becomes to operate shelters. When the number of evacuees decreases, the operation costs can be reduced by moving the remaining evacuees to other shelters and closing shelters as quickly as possible. On the other hand, relocation between shelters imposes a huge emotional burden on evacuees. In this study, we formulate the "Evacuation Shelter Scheduling Problem," which allocates evacuees to shelters in such a way to minimize the movement costs of the evacuees and the operation costs of the shelters. Since it is difficult to solve this quadratic programming problem directly, we show its transformation into a 0-1 integer programming problem. In addition, such a formulation struggles to calculate the burden of relocating them from historical data because no payments are actually made. To solve this issue, we propose a method that estimates movement costs based on the numbers of evacuees and shelters during an actual disaster. Simulation experiments with records from the Kobe earthquake (Great Hanshin-Awaji Earthquake) showed that our proposed method reduced operation costs by 33.7 million dollars: 32%.