32 citations · 64 across the 11 of their papers we have counts for
11 papers
A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems
Xavier Gonzalez, E. Kelly Buchanan, Hyun Dong Lee +6
Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes…
Analysis of singular subspaces under random perturbations
Ke Wang
We present a comprehensive analysis of singular vector and singular subspace perturbations in the signal-plus-noise matrix model with random Gaussian noise. Assuming a low-rank sig…
Sequence Modeling with Multiresolution Convolutional Memory
Jiaxin Shi, Ke Alexander Wang, Emily B. Fox
Efficiently capturing the long-range patterns in sequential data sources salient to a given task -- such as classification and generative modeling -- poses a fundamental challenge.…
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi +1
Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glu…
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
Ke Alexander Wang, Danielle Maddix, Yuyang Wang
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure. We present GO…
Is Importance Weighting Incompatible with Interpolating Classifiers?
Ke Alexander Wang, Niladri S. Chatterji, Saminul Haque +1
Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance…