Arash Behboodi I am machine learning research scientist and Director of Engineering at Qualcomm AI Research. My research interests are at the intersection of machine learning, mathematical signal processing and information theory and include in particular learning theory, machine learning for inverse problems, compressed sensing, geometric deep learning and differentiable simulation. I am working as well on machine learning design for wireless communication systems.

News and Highlights

• New paper: Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving, ICLR 2025 Workshop on Reasoning and Planning for Large Language Models

• Accepted paper at ICLR 2025: Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits (Spotlight)

• Accepted paper at ICLR 2025: Probabilistic and Differentiable Wireless Simulation with Geometric Transformers

• Accepted paper at TMLR: On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing

• Accepted paper at TMLR: Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach

• Accepted paper at NeurIPs 2024: An Information Theoretic Perspective on Conformal Prediction

• We are organizing the workshop D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers at NeurIPS 2024.

Selected recent papers

• Sara Rajaee, Kumar Pratik, Gabriele Cesa, Arash Behboodi, Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving, ICLR 2025 Workshop on Reasoning and Planning for Large Language Models

• Ashish Khisti, M.Reza Ebrahimi, Hassan Dbouk, Arash Behboodi, Roland Memisevic, Christos Louizos, Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits , ICLR 2025 (Spotlight), also appeared at ICML 2024 Workshop on Theoretical Foundations of Foundation Models

• Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer, Probabilistic and Differentiable Wireless Simulation with Geometric Transformers , ICLR 2025, also appeared at ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling 2024

• Arash Behboodi, Gabriele Cesa, On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing , TMLR, 2025

• Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi, Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach , TMLR 2025, also appeared in Symmetry and Geometry in Neural Representations - NeurReps Workshop, NeurIPS 2023

• Fabio Valerio Massoli, Tim Bakker, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi, Simulating, Fast and Slow: Learning Policies for Black-Box Optimization , Preprint 2024

• Alvaro H.C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi, An Information Theoretic Perspective on Conformal Prediction , NeurIPS 2024

• Fabio Valerio Massoli, Christos Louizos, Arash Behboodi, Variational Learning LISTA , TMLR, July 2024