Search Results for author:
Found 13 papers, 7 papers with code
Causal Discovery under Off-Target Interventions
Causal graph discovery is a significant problem with applications across various disciplines.
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.
Adaptivity Complexity for Causal Graph Discovery
For this problem, we provide a $r$-adaptive algorithm that achieves $O(\min\{r,\log n\} \cdot n^{1/\min\{r,\log n\}})$ approximation with respect to the verification number, a well-known lower bound for adaptive algorithms.
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 $G=G^*$.
New metrics and search algorithms for weighted causal DAGs
Recovering causal relationships from data is an important problem.
Subset verification and search algorithms for causal DAGs
In this work, we study the problem of identifying the smallest set of interventions required to learn the causal relationships between a subset of edges (target edges).
Learning and Testing Latent-Tree Ising Models Efficiently
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.
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.
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.
The Complexity of Sparse Tensor PCA
Even in the restricted case of sparse PCA, known algorithms only recover the sparse vectors for $\lambda \geq \tilde{\mathcal{O}}(k \cdot r)$ while our algorithms require $\lambda \geq \tilde{\mathcal{O}}(k)$.
Massively Parallel Correlation Clustering in Bounded Arboricity Graphs
Identifying clusters of similar elements in a set is a common task in data analysis.
Graph Matching
Distributed, Parallel, and Cluster Computing
Data Structures and Algorithms
k-means++: few more steps yield constant approximation
The k-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is a state-of-the-art algorithm for solving the k-means clustering problem and is known to give an O(log k)-approximation in expectation.
Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures
We take a data driven approach to rank these structures by using neural networks to predict the presence of substructures given the mass spectrum, and matching these substructures to the candidate structures.
