Search Results for author:
Found 14 papers, 7 papers with code
Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation
Generalizing work of Acharya et al. 2016 on the utility of approximate PML we show that our algorithm provides a nearly linear time universal plug-in estimator for all symmetric functions up to accuracy $\epsilon = \Omega(n^{-0. 166})$.
A General Framework for Symmetric Property Estimation
In this paper we provide a general framework for estimating symmetric properties of distributions from i. i. d.
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood
For each problem we provide polynomial time algorithms that given $n$ i. i. d.\ samples from a discrete distribution, achieve an approximation factor of $\exp\left(-O(\sqrt{n} \log n) \right)$, improving upon the previous best-known bound achievable in polynomial time of $\exp(-O(n^{2/3} \log n))$ (Charikar, Shiragur and Sidford, 2019).
Instance Based Approximations to Profile Maximum Likelihood
In this paper we provide a new efficient algorithm for approximately computing the profile maximum likelihood (PML) distribution, a prominent quantity in symmetric property estimation.
Fractionally Log-Concave and Sector-Stable Polynomials: Counting Planar Matchings and More
While perfect matchings on planar graphs can be counted exactly in polynomial time, counting non-perfect matchings was shown by [Jer87] to be #P-hard, who also raised the question of whether efficient approximate counting is possible.
Point Processes
Data Structures and Algorithms
Combinatorics
Probability
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.
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood
We provide an efficient unified plug-in approach for estimating symmetric properties of distributions given $n$ independent samples.
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).
New metrics and search algorithms for weighted causal DAGs
Recovering causal relationships from data is an important problem.
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.
Meek Separators and Their Applications in Targeted Causal Discovery
In our work, we focus on two such well-motivated problems: subset search and causal matching.
Testing with Non-identically Distributed Samples
From a learning standpoint, even with $c=1$ samples from each distribution, $\Theta(k/\varepsilon^2)$ samples are necessary and sufficient to learn $\textbf{p}_{\mathrm{avg}}$ to within error $\varepsilon$ in TV distance.
Causal Discovery under Off-Target Interventions
Causal graph discovery is a significant problem with applications across various disciplines.
Membership Testing in Markov Equivalence Classes via Independence Query Oracles
While learning involves the task of recovering the Markov equivalence class (MEC) of the underlying causal graph from observational data, the testing counterpart addresses the following critical question: Given a specific MEC and observational data from some causal graph, can we determine if the data-generating causal graph belongs to the given MEC?
