1 citations · 1 across the 7 of their papers we have counts for
9 papers
Estimating Ising Models in Total Variation Distance
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We consider the problem of estimating Ising models over variables in Total Variation (TV) distance, given independent samples from the model. While the statistical complexi…
Learning Gaussian DAG Models without Condition Number Bounds
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We study the problem of learning the topology of a directed Gaussian Graphical Model under the equal-variance assumption, where the graph has nodes and maximum in-degree . P…
The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interference
Vardis Kandiros, Charilaos Pipis, Constantinos Daskalakis +1
A fundamental problem in network experiments is selecting an appropriate experimental design in order to precisely estimate a given causal effect of interest. In this work, we prop…
On Sampling from Ising Models with Spectral Constraints
Andreas Galanis, Alkis Kalavasis, Anthimos Vardis Kandiros
We consider the problem of sampling from the Ising model when the underlying interaction matrix has eigenvalues lying within an interval of length . Recent work in this setting…
Learning Hard-Constrained Models with One Sample
Andreas Galanis, Alkis Kalavasis, Anthimos Vardis Kandiros
We consider the problem of estimating the parameters of a Markov Random Field with hard-constraints using a single sample. As our main running examples, we use the -SAT and the…
EM's Convergence in Gaussian Latent Tree Models
Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros
We study the optimization landscape of the log-likelihood function and the convergence of the Expectation-Maximization (EM) algorithm in latent Gaussian tree models, i.e. tree-stru…
Learning and Testing Latent-Tree Ising Models Efficiently
Davin Choo, Yuval Dagan, Constantinos Daskalakis +1
We provide time- and sample-efficient algorithms for learning and testing latent-tree Ising models, i.e. Ising models that may only be observed at their leaf nodes. On the learning…
Statistical Estimation from Dependent Data
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +2
We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioned on their feature vectors, but dependent, ca…
Learning Ising models from one or multiple samples
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +1
There have been two separate lines of work on estimating Ising models: (1) estimating them from multiple independent samples under minimal assumptions about the model's interaction…