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20242026
most citedSCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees

1 citations · 1 across the 17 of their papers we have counts for

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stat.ML2026

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…

cs.IT2026

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…

stat.ML2026

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 (…

stat.ML2026

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…

cs.LG2026

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…

cs.AI2026

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…