Amirmohammad Farzaneh

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, current approaches to solving the IB problem rely on a heuristic tuning of hyperparameters, offering no guarantees that the learned features satisfy information-theoretic constraints. In this work, we introduce a statistically valid solution to this problem, referred to as IB via multiple hypothesis testing (IB-MHT), which ensures that the learned features meet the IB constraints with high probability, regardless of the size of the available dataset. The proposed methodology builds on Pareto testing and learn-then-test (LTT), and it wraps around existing IB solvers to provide statistical guarantees on the IB constraints. We demonstrate the performance of IB-MHT on classical and deterministic IB formulations, including experiments on distillation of language models. The results validate the effectiveness of IB-MHT in outperforming conventional methods in terms of statistical robustness and reliability.

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 framing hyperparameter selection as a multiple hypothesis testing problem, recent research has shown that it is possible to provide statistical guarantees on population risk measures attained by the selected hyperparameter. This paper reviews the Learn-Then-Test (LTT) framework, which formalizes this approach, and explores several extensions tailored to engineering-relevant scenarios. These extensions encompass different risk measures and statistical guarantees, multi-objective optimization, the incorporation of prior knowledge and dependency structures into the hyperparameter selection process, as well as adaptivity. The paper also includes illustrative applications for communication systems.

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 relationships between the expected reliability levels of the hyperparameters can be inferred from prior information and held-out data -- e.g., longer prompt templates may be more detailed and thus more reliable. However, existing hyperparameter selection methods either do not provide formal reliability guarantees or are unable to incorporate structured knowledge in the hyperparameter space. This paper introduces reliability graph-based Pareto testing (RG-PT), a novel multi-objective hyperparameter selection framework that maintains formal reliability guarantees in terms of false discovery rate (FDR), while accounting for known relationships among hyperparameters via a directed acyclic graph. Edges in the graph reflect expected reliability and cost trade-offs among hyperparameters, which are inferred via the Bradley-Terry (BT) ranking model from prior information and held-out data. Experimental evaluations demonstrate that RG-PT significantly outperforms existing methods such as learn-then-test (LTT) and Pareto testing (PT) through a more efficient exploration of the hyperparameter space.

Amirmohammad Farzaneh, Osvaldo Simeone

Online anomaly detection is essential in fields such as cybersecurity, healthcare, and industrial monitoring, where promptly identifying deviations from expected behavior can avert critical failures or security breaches. While numerous anomaly scoring methods based on supervised or unsupervised learning have been proposed, current approaches typically rely on a continuous stream of real-world calibration data to provide assumption-free guarantees on the false discovery rate (FDR). To address the inherent challenges posed by limited real calibration data, we introduce context-aware prediction-powered conformal online anomaly detection (C-PP-COAD). Our framework strategically leverages synthetic calibration data to mitigate data scarcity, while adaptively integrating real data based on contextual cues. C-PP-COAD utilizes conformal p-values, active p-value statistics, and online FDR control mechanisms to maintain rigorous and reliable anomaly detection performance over time. Experiments conducted on both synthetic and real-world datasets demonstrate that C-PP-COAD significantly reduces dependency on real calibration data without compromising guaranteed FDR control.