1 citations · 1 across the 17 of their papers we have counts for
18 papers
Risk-Averse Decision Making with Multi-Level Reliability Guarantees
Amirmohammad Farzaneh, Osvaldo Simeone
Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels. This paper studies the p…
Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +2
To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition…
Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
Amirmohammad Farzaneh, Osvaldo Simeone
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (…
Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
Amirmohammad Farzaneh, Osvaldo Simeone
LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI syst…
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…
Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control
Amirmohammad Farzaneh, Salvatore D'Oro, Osvaldo Simeone
Large language model (LLM)-powered agents can translate high-level user intents into plans and actions in an environment. Yet after observing an outcome, users may wonder: What if…
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…
SCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees
Weicao Deng, Binpu Shi, Min Li +1
As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditio…
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…
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +1
Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal a…
Statistically Valid Information Bottleneck via Multiple Hypothesis Testing
Amirmohammad Farzaneh, Osvaldo Simeone
The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, cur…
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…
Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems
Houssem Sifaou, Osvaldo Simeone
In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learn…
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…
CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming
Weicao Deng, Min Li, Ming-Min Zhao +2
Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduc…