Search Results for author:
Found 22 papers, 6 papers with code
Outlier Robust Multivariate Polynomial Regression
Furthermore, we show that it is possible to have the run-time be independent of $1/\sigma$, at the cost of a higher sample complexity.
Optimal estimation of Gaussian (poly)trees
We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data.
Learning bounded-degree polytrees with known skeleton
We establish finite-sample guarantees for efficient proper learning of bounded-degree polytrees, a rich class of high-dimensional probability distributions and a subclass of Bayesian networks, a widely-studied type of graphical model.
Total Variation Distance Estimation Is as Easy as Probabilistic Inference
In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models.
Active causal structure learning with advice
When the advice is a DAG $G$, we design an adaptive search algorithm to recover $G^*$ whose intervention cost is at most $O(\max\{1, \log \psi\})$ times the cost for verifying $G^*$; here, $\psi$ is a distance measure between $G$ and $G^*$ that is upper bounded by the number of variables $n$, and is exactly 0 when $G=G^*$.
Near-Optimal Degree Testing for Bayes Nets
This paper considers the problem of testing the maximum in-degree of the Bayes net underlying an unknown probability distribution $P$ over $\{0, 1\}^n$, given sample access to $P$.
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a large amount of i. i. d.
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.
Verification and search algorithms for causal DAGs
Our result is the first known algorithm that gives a non-trivial approximation guarantee to the verifying size on general unweighted graphs and with bounded size interventions.
Independence Testing for Bounded Degree Bayesian Network
We study the following independence testing problem: given access to samples from a distribution $P$ over $\{0, 1\}^n$, decide whether $P$ is a product distribution or whether it is $\varepsilon$-far in total variation distance from any product distribution.
Efficient inference of interventional distributions
For sets $\mathbf{X},\mathbf{Y}\subseteq \mathbf{V}$, and setting ${\bf x}$ to $\mathbf{X}$, let $P_{\bf x}(\mathbf{Y})$ denote the interventional distribution on $\mathbf{Y}$ with respect to an intervention ${\bf x}$ to variables ${\bf x}$.
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
We also study a couple of new algorithms for the problem: - BatchAvgLeastSquares takes the average of several batches of least squares solutions at each node, so that one can interpolate between the batch size and the number of batches.
Identifiability of AMP chain graph models
AMP models are described by DAGs on chain components which themselves are undirected graphs.
Testing Product Distributions: A Closer Look
We study the problems of identity and closeness testing of $n$-dimensional product distributions.
Eco-Routing Using Open Street Maps
A vehicle's fuel consumption depends on its type, the speed, the condition, and the gradients of the road on which it is moving.
Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu
For a distribution $P$ on $\Sigma^n$ and a tree $T$ on $n$ nodes, we say $T$ is an $\varepsilon$-approximate tree for $P$ if there is a $T$-structured distribution $Q$ such that $D(P\;||\;Q)$ is at most $\varepsilon$ more than the best possible tree-structured distribution for $P$.
Efficient Statistics for Sparse Graphical Models from Truncated Samples
We show that ${\mu}$ and ${\Sigma}$ can be estimated with error $\epsilon$ in the Frobenius norm, using $\tilde{O}\left(\frac{\textrm{nz}({\Sigma}^{-1})}{\epsilon^2}\right)$ samples from a truncated $\mathcal{N}({\mu},{\Sigma})$ and having access to a membership oracle for $S$.
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions.
Learning and Sampling of Atomic Interventions from Observations
Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(n\epsilon^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq \epsilon$, and: 1.
Learning and Testing Causal Models with Interventions
We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009).
Fishing out Winners from Vote Streams
We investigate the problem of winner determination from computational social choice theory in the data stream model.
On learning k-parities with and without noise
We improve the previous best result of Buhrman et al. by an $\exp(k)$ factor in the time complexity.
