1 citations · 1 across the 4 of their papers we have counts for
6 papers
Observation geometry for uncertainty-aware Hamiltonian inference and experimental design in quantum magnets
Roy Liu, Venugopal Ranganathan, David Dahlbom +12
Determining microscopic interactions from spectroscopic and scattering measurements is central to understanding quantum materials, yet it often remains unclear which interactions c…
Separation-Utility Pareto Frontier: An Information-Theoretic Characterization
Shizhou Xu
We study the Pareto frontier (optimal trade-off) between utility and separation, a fairness criterion requiring predictive independence from sensitive attributes conditional on the…
Machine Unlearning via Information Theoretic Regularization
Shizhou Xu, Thomas Strohmer
How can we effectively remove or ``unlearn'' undesirable information, such as specific features or the influence of individual data points, from a learning outcome while minimizing…
Multi-resolution Enhancement for Full Spectrum Neural Representations
Yuan Ni, Zhantao Chen, Shizhou Xu +5
Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (IN…
Forgetting-MarI: LLM Unlearning via Marginal Information Regularization
Shizhou Xu, Yuan Ni, Stefan Broecker +1
As AI models are trained on ever-expanding datasets, the ability to remove the influence of specific data from trained models has become essential for privacy protection and regula…
WHOMP: Optimizing Randomized Controlled Trials via Wasserstein Homogeneity
Shizhou Xu, Thomas Strohmer
We investigate methods for partitioning datasets into subgroups that maximize diversity within each subgroup while minimizing dissimilarity across subgroups. We introduce a novel p…