24 citations · 35 across the 10 of their papers we have counts for
6 papers · 1 filter
How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories
Younhun Kim, Frederic Koehler, Ankur Moitra +2
Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attemp…
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…
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis
Frederic Koehler, Andrej Risteski
There has been a large amount of interest, both in the past and particularly recently, into the power of different families of universal approximators, e.g. ReLU networks, polynomi…
Learning Restricted Boltzmann Machines via Influence Maximization
Guy Bresler, Frederic Koehler, Ankur Moitra +1
Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for l…
The Vertex Sample Complexity of Free Energy is Polynomial
Vishesh Jain, Frederic Koehler, Elchanan Mossel
We study the following question: given a massive Markov random field on nodes, can a small sample from it provide a rough approximation to the free energy $\mathcal{F}_n = \log…
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity
Vishesh Jain, Frederic Koehler, Elchanan Mossel
The mean field approximation to the Ising model is a canonical variational tool that is used for analysis and inference in Ising models. We provide a simple and optimal bound for t…