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Optimized Certainty Equivalent Risk-Controlling Prediction Sets
Jiayi Huang, Amirmohammad Farzaneh, Osvaldo Simeone
In safety-critical applications such as medical image segmentation, prediction systems must provide reliability guarantees that extend beyond conventional expected loss control. Wh…
Multi-Fidelity Hybrid Reinforcement Learning via Information Gain Maximization
Houssem Sifaou, Osvaldo Simeone
Optimizing a reinforcement learning (RL) policy typically requires extensive interactions with a high-fidelity simulator of the environment, which are often costly or impractical.…
Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
Amirmohammad Farzaneh, Matteo Zecchin, Osvaldo Simeone
This work addresses the problem of constructing reliable prediction intervals for individual counterfactual outcomes. Existing conformal counterfactual inference (CCI) methods prov…
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
Amirmohammad Farzaneh, Osvaldo Simeone
Online anomaly detection is essential in fields such as cybersecurity, healthcare, industrial monitoring, and telecommunications, where promptly identifying deviations from expecte…
Ensuring Reliability via Hyperparameter Selection: Review and Advances
Amirmohammad Farzaneh, Osvaldo Simeone
Hyperparameter selection is a critical step in the deployment of artificial intelligence (AI) models, particularly in the current era of foundational, pre-trained, models. By frami…
Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs
Amirmohammad Farzaneh, Osvaldo Simeone
The selection of hyperparameters, such as prompt templates in large language models (LLMs), must often strike a balance between reliability and cost. In many cases, structural rela…
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
Haifeng Wen, Hong Xing, Osvaldo Simeone
For modern artificial intelligence (AI) applications such as large language models (LLMs), the training paradigm has recently shifted to pre-training followed by fine-tuning. Furth…
Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck
Yuzhen Ke, Zoran Utkovski, Mehdi Heshmati +3
An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server. The device carries…