Stochastic Bouncy Particle Sampler. - Ari Pakman; Dar Gilboa; David E. Carlson; Liam Paninski (ICML, 2017)

Efficient Dictionary Learning with Gradient Descent. - Dar Gilboa; Sam Buchanan; John Wright 001 (ICML, 2019)

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. - Yaniv Blumenfeld; Dar Gilboa; Daniel Soudry (NeurIPS, 2019)

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs. - Dar Gilboa; Bo Chang 002; Minmin Chen; Greg Yang; Samuel S. Schoenholz; Ed H. Chi; Jeffrey Pennington (CoRR, 2019)

Wider Networks Learn Better Features. - Dar Gilboa; Guy Gur-Ari (CoRR, 2019)

Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? - Yaniv Blumenfeld; Dar Gilboa; Daniel Soudry (ICML, 2020)

When Can Nonconvex Optimization Problems be Solved with Gradient Descent? A Few Case Studies. - Dar Gilboa (2020)

Deep Networks and the Multiple Manifold Problem. - Sam Buchanan; Dar Gilboa; John Wright 001 (ICLR, 2021)

Estimating the Unique Information of Continuous Variables. - Ari Pakman; Amin Nejatbakhsh; Dar Gilboa; Abdullah Makkeh; Luca Mazzucato; Michael Wibral; Elad Schneidman (NeurIPS, 2021)

Deep Networks Provably Classify Data on Curves. - Tingran Wang; Sam Buchanan; Dar Gilboa; John Wright 001 (NeurIPS, 2021)

Estimating the Unique Information of Continuous Variables. - Ari Pakman; Dar Gilboa; Elad Schneidman (CoRR, 2021)

Marginalizable Density Models. - Dar Gilboa; Ari Pakman; Thibault Vatter (CoRR, 2021)

On quantum backpropagation, information reuse, and cheating measurement collapse. - Amira Abbas; Robbie King; Hsin-Yuan Huang; William J. Huggins; Ramis Movassagh; Dar Gilboa; Jarrod R. McClean (NeurIPS, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Exponential Quantum Communication Advantage in Distributed Inference and Learning. - Dar Gilboa; Hagay Michaeli; Daniel Soudry; Jarrod R. McClean (NeurIPS, 2024)

Consumable Data via Quantum Communication. - Dar Gilboa; Siddhartha Jain 002; Jarrod R. McClean (Electron. Colloquium Comput. Complex., 2024)

Quantum advantage for learning shallow neural networks with natural data distributions. - Laura Lewis; Dar Gilboa; Jarrod R. McClean (CoRR, 2025)