24 citations · 35 across the 10 of their papers we have counts for
12 papers · 1 filter
Multidimensional Scaling: Approximation and Complexity
Erik Demaine, Adam Hesterberg, Frederic Koehler +2
Metric Multidimensional scaling (MDS) is a classical method for generating meaningful (non-linear) low-dimensional embeddings of high-dimensional data. MDS has a long history in th…
On the Power of Preconditioning in Sparse Linear Regression
Jonathan Kelner, Frederic Koehler, Raghu Meka +1
Sparse linear regression is a fundamental problem in high-dimensional statistics, but strikingly little is known about how to efficiently solve it without restrictive conditions on…
Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, Andrej Risteski
Normalizing flows are among the most popular paradigms in generative modeling, especially for images, primarily because we can efficiently evaluate the likelihood of a data point.…
From Boltzmann Machines to Neural Networks and Back Again
Surbhi Goel, Adam Klivans, Frederic Koehler
Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this wor…
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
Frederic Koehler
Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning and inference in graphical models. While it is known to be exact on trees, in most applica…
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective
Vishesh Jain, Frederic Koehler, Andrej Risteski
The free energy is a key quantity of interest in Ising models, but unfortunately, computing it in general is computationally intractable. Two popular (variational) approximation sc…