Deep neural networks are biased towards simple functions. - Giacomo De Palma; Bobak Toussi Kiani; Seth Lloyd (CoRR, 2018)

Random deep neural networks are biased towards simple functions. - Giacomo De Palma; Bobak Toussi Kiani; Seth Lloyd (NeurIPS, 2019)

Learning Unitaries by Gradient Descent. - Bobak Toussi Kiani; Seth Lloyd; Reevu Maity (CoRR, 2020)

Quantum advantage for differential equation analysis. - Bobak Toussi Kiani; Giacomo De Palma; Dirk R. Englund; William M. Kaminsky; Milad Marvian; Seth Lloyd (CoRR, 2020)

Adversarial Robustness Guarantees for Random Deep Neural Networks. - Giacomo De Palma; Bobak Toussi Kiani; Seth Lloyd (ICML, 2021)

Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data. - Bobak Toussi Kiani; Giacomo De Palma; Milad Marvian; Zi-Wen Liu; Seth Lloyd (CoRR, 2021)

Quantum algorithms for group convolution, cross-correlation, and equivariant transformations. - Grecia Castelazo; Quynh The Nguyen; Giacomo De Palma; Dirk R. Englund; Seth Lloyd; Bobak Toussi Kiani (CoRR, 2021)

Implicit Bias of Linear Equivariant Networks. - Hannah Lawrence; Bobak Toussi Kiani; Kristian G. Georgiev; Andrew K. Dienes (ICML, 2022)

projUNN: efficient method for training deep networks with unitary matrices. - Bobak Toussi Kiani; Randall Balestriero; Yann LeCun; Seth Lloyd (NeurIPS, 2022)

Quantum algorithm for dense kernel matrices using hierarchical splitting. - Quynh The Nguyen; Bobak Toussi Kiani; Seth Lloyd (CoRR, 2022)

Joint Embedding Self-Supervised Learning in the Kernel Regime. - Bobak Toussi Kiani; Randall Balestriero; Yubei Chen; Seth Lloyd; Yann LeCun (CoRR, 2022)

Block-encoding dense and full-rank kernels using hierarchical matrices: applications in quantum numerical linear algebra. - Quynh T. Nguyen; Bobak T. Kiani; Seth Lloyd (Quantum, 2022)

The SSL Interplay: Augmentations, Inductive Bias, and Generalization. - Vivien Cabannes; Bobak Toussi Kiani; Randall Balestriero; Yann LeCun; Alberto Bietti (ICML, 2023)

Equivariant Polynomials for Graph Neural Networks. - Omri Puny; Derek Lim; Bobak Toussi Kiani; Haggai Maron; Yaron Lipman (ICML, 2023)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations. - Grégoire Mialon; Quentin Garrido; Hannah Lawrence; Danyal Rehman; Yann LeCun; Bobak T. Kiani (NeurIPS, 2023)

Neural Networks for Programming Quantum Annealers. - Samuel Bosch; Bobak Toussi Kiani; Rui Yang; Adrian Lupascu; Seth Lloyd (CoRR, 2023)

Horizons of Artificial Intelligence in Quantum Computation - Bobak T. Kiani (2023)

Efficient classical algorithms for simulating symmetric quantum systems. - Eric R. Anschütz; Andreas Bauer; Bobak Toussi Kiani; Seth Lloyd (Quantum, 2023)

On the hardness of learning under symmetries. - Bobak T. Kiani; Thien Le; Hannah Lawrence; Stefanie Jegelka; Melanie Weber 001 (ICLR, 2024)

Unitary Convolutions for Learning on Graphs and Groups. - Bobak T. Kiani; Lukas Fesser; Melanie Weber 001 (NeurIPS, 2024)

Hardness of Learning Neural Networks under the Manifold Hypothesis. - Bobak T. Kiani; Jason Wang; Melanie Weber 001 (NeurIPS, 2024)

Combinatorial NLTS From the Overlap Gap Property. - Eric R. Anschütz; David Gamarnik; Bobak T. Kiani (Quantum, 2024)

Optimizing Sparse SYK. - Matthew Ding 001; Robbie King; Bobak T. Kiani; Eric R. Anschütz (CoRR, 2025)