This paper proposes an AI-enhanced Marketing Mix Modeling (MMM) framework that integrates machine learning (ML), explainable AI (XAI), and large language models (LLMs) to evaluate marketing effectiveness with improved predictive accuracy, interpretability, and practical applicability. Moving beyond traditional MMM approaches, the framework employs the XGBoost algorithm to capture nonlinear relationships between multichannel marketing investments and business outcomes. SHAP analysis further enhances model interpretability through feature-importance rankings, beeswarm visualizations, and dependence plots that quantify each channel’s marginal contribution. In […]

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arXiv:2406.01552v2 Announce Type: replace Abstract: Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to produce and handle tensors is essential to efficiently address problems in these domains. In this paper, we show how to exploit the underlying symmetries of functions that map tensors to tensors. More concretely, we develop universally expressive equivariant machine learning architectures on tensors that exploit that, in […]

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Scaling federated learning (FL) to billion-parameter models introduces critical trade-offs between communication efficiency, model accuracy, and privacy guarantees. Existing solutions often tackle these challenges in isolation, sacrificing accuracy or relying on costly cryptographic tools. We propose ERIS, a serverless FL framework that balances privacy and accuracy while eliminating the server bottleneck and distributing the communication load. ERIS combines a model partitioning strategy, distributing aggregation across multiple client-side aggregators, with a distributed shifted gradient compression mechanism. We theoretically prove […]

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Working extensively with AWS credentials in Kubernetes this quarter revealed how often credential precedence causes configuration issues. While the AWS SDK’s credential chain is well-designed, understanding the priority order is crucial for production deployments. Here’s what I’ve learned. The Problem Nobody Talks About A recent incident illustrated this well: We configured IRSA for a microservice, validated it in staging, and deployed to production successfully. Two weeks later, an audit revealed the service was using broader IAM permissions than […]

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Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost. This tutorial highlights a shift in optimization: using differentiable programming not only to execute algorithms but to learn how to design them. Modern frameworks such as PyTorch, TensorFlow, and JAX enable this paradigm through efficient automatic differentiation. Embedding first-order methods within these systems allows end-to-end training that improves convergence and solution quality. Guided by Fenchel-Rockafellar duality, the tutorial demonstrates how duality-informed iterative schemes such as ADMM […]

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arXiv:2601.09726v1 Announce Type: new Abstract: Large Language Models (LLMs) are often evaluated against ideals of perfect Bayesian inference, yet growing evidence suggests that their in-context reasoning exhibits systematic forgetting of past information. Rather than viewing this behavior as a limitation, we reinterpret forgetting as a functional cognitive mechanism. Drawing inspiration from human memory dynamics, we model LLM inference as a probabilistic memory process governed by exponential decay. We introduce a benchmark suite that evaluates temporal reasoning, concept drift […]

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arXiv:2601.05245v1 Announce Type: cross Abstract: We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner’s predictions, we prove an $Omega(T^{2/3})$ lower bound on expected multicalibration error using just three disjoint binary groups. This matches the upper bounds of Noarov et al. (2025) up to logarithmic factors and exceeds the $O(T^{2/3-varepsilon})$ upper bound for marginal calibration (Dagan et al., […]

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