arXiv:2507.05526v2 Announce Type: replace-cross Abstract: In scientific domains — from biology to the social sciences — many questions boil down to textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph) are known, it is possible to estimate the intervention distributions. In the absence of this domain knowledge, the causal structure must be discovered from the available observational data. However, observational data are often compatible with multiple causal graphs, […]

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Anyone who works with PyTorch model code starts asking the same questions: Why is this taking so long? How do I make my training loop faster? Whether you’re an ML engineer, a researcher or just decided to play around with a random ML repository over the weekend, you will eventually try to understand how to speed your code up. However, before we can do that, we need to learn how to measure performance correctly. And then draw the […]

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Hi everyone, I am an RL novice working on my first “real” project: a solver for the Multi-Warehouse Vehicle Routing Problem (MWVRP). My background is limited (I’ve essentially only read the DeepMDV paper and some standard VRP literature), so I am looking for a sanity check on my approach, as well as recommendations for papers or codebases that tackle similar constraints. The Problem Setting: I am modeling a supply chain with: Multiple Depots & Heterogeneous Fleet (Vans, Medium […]

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arXiv:2602.03965v1 Announce Type: new Abstract: Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple users observe anomalies to the same destination, this may reflect shared infrastructure, routing behavior, or congestion. Inferring such shared behavior is challenging because anomaly magnitudes vary widely across devices, even within the same ISP and geographic area, and detailed network topology information is often unavailable. We study whether devices experiencing a shared latency […]

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Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics — inherent to wireless systems due to fading, noise, and traffic mobility — remains insufficiently understood. We address this gap by evaluating Bellman-based (Conservative Q-Learning), sequence-based (Decision Transformers), and hybrid (Critic-Guided Decision Transformers) offline RL methods in […]

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Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to […]

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When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations. This game-theoretic perspective reveals that existing robust optimization methods such as minimizing worst-group loss or regret correspond to classical bargaining solutions and embody different fairness principles. We propose relative improvement, the ratio of actual risk reduction to potential reduction from a baseline predictor, which recovers the Kalai-Smorodinsky solution. Unlike absolute-scale methods that may not be […]

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