233 citations · 531 across the 32 of their papers we have counts for
6 papers · 1 filter
Slow Mixing of Quantum Gibbs Samplers
David Gamarnik, Bobak T. Kiani, Alexander Zlokapa
Preparing thermal (Gibbs) states is a common task in physics and computer science. Recent algorithms mimic cooling via system-bath coupling, where the cost is determined by mixing…
Unitary convolutions for learning on graphs and groups
Bobak T. Kiani, Lukas Fesser, Melanie Weber
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-inva…
Strongly interacting fermions are non-trivial yet non-glassy
Eric R. Anschuetz, Chi-Fang Chen, Bobak T. Kiani +1
Random spin systems at low temperatures are glassy and feature computational hardness in finding low-energy states. We study the random all-to-all interacting fermionic Sachdev--Ye…
Hardness of Learning Neural Networks under the Manifold Hypothesis
Bobak T. Kiani, Jason Wang, Melanie Weber
The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding geometric structure has been demonstrated empi…
Bounds on the ground state energy of quantum -spin Hamiltonians
Eric R. Anschuetz, David Gamarnik, Bobak T. Kiani
We consider the problem of estimating the ground state energy of quantum -local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass mode…
On the hardness of learning under symmetries
Bobak T. Kiani, Thien Le, Hannah Lawrence +2
We study the problem of learning equivariant neural networks via gradient descent. The incorporation of known symmetries ("equivariance") into neural nets has empirically improved…