activity
20182026
most citedA Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

19 citations · 32 across the 13 of their papers we have counts for

collaborators

13 papers

cs.LG2026

PROMA: Projected Microbatch Accumulation for Reference-Free Proximal Policy Updates

Nilin Abrahamsen

This note introduces Projected Microbatch Accumulation (PROMA), a reference-free proximal policy method that controls KL divergence by projecting away high-variance components of t…

cs.LG2025

ISOPO: Proximal policy gradients without pi-old

Nilin Abrahamsen

This note introduces Isometric Policy Optimization (ISOPO), an efficient method to approximate the natural policy gradient in a single gradient step. In comparison, existing proxim…

physics.comp-ph2024★ 19 cited

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Gil Goldshlager, Nilin Abrahamsen, Lin Lin

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small m…

cs.CV2023

Inventing art styles with no artistic training data

Nilin Abrahamsen, Jiahao Yao

We propose two procedures to create painting styles using models trained only on natural images, providing objective proof that the model is not plagiarizing human art styles. In t…

math.NA2023★ 1 cited

Anti-symmetric Barron functions and their approximation with sums of determinants

Nilin Abrahamsen, Lin Lin

A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The Barron space consists of high-dime…

cs.LG2023★ 1 cited

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

Nilin Abrahamsen, Zhiyan Ding, Gil Goldshlager +1

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We…

cs.LG2022

Efficient anti-symmetrization of a neural network layer by taming the sign problem

Nilin Abrahamsen, Lin Lin

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum…

quant-ph2022★ 3 cited

Entanglement area law for 1D gauge theories and bosonic systems

Nilin Abrahamsen, Yu Tong, Ning Bao +2

We prove an entanglement area law for a class of 1D quantum systems involving infinite-dimensional local Hilbert spaces. This class of quantum systems include bosonic models such a…

quant-ph2020

Short proof of a spectral Chernoff bound for local Hamiltonians

Nilin Abrahamsen

We give a simple proof of a Chernoff bound for the spectrum of a -local Hamiltonian based on Weyl's inequalities. The complexity of estimating the spectrum's -th quantile…

quant-ph2020

Solving degenerate 2D frustration-free spin systems in sub-exponential time

Nilin Abrahamsen

We give an improved analysis of approximate ground space projectors (AGSPs) to obtain a sharp error reduction bound in the setting a degenerate ground space. The improved tools imp…

quant-ph2020★ 3 cited

Sharp implications of AGSPs for degenerate ground spaces

Nilin Abrahamsen

We generalize the `off-the-rack' AGSPentanglement bound implication of [Arad, Landau, and Vazirani '12] from unique ground states to degenerate ground spaces. Our cond…

quant-ph2019★ 4 cited

A polynomial-time algorithm for ground states of spin trees

Nilin Abrahamsen

We prove that the ground states of a local Hamiltonian satisfy an area law and can be computed in polynomial time when the interaction graph is a tree with discrete fractal dimensi…

stat.ML2018★ 1 cited

Sparse Gaussian ICA

Nilin Abrahamsen, Philippe Rigollet

Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS wher…