4 code implementations • 30 Jun 2022 • Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya
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.
3 code implementations • 9 Jan 2023 • Davin Choo, Kirankumar Shiragur
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).
1 code implementation • 13 Feb 2024 • Davin Choo, Kirankumar Shiragur, Caroline Uhler
Causal graph discovery is a significant problem with applications across various disciplines.
no code implementations • 21 May 2019 • Moses Charikar, Kirankumar Shiragur, Aaron Sidford
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 $\epsilon = \Omega(n^{-0. 166})$.
1 code implementation • NeurIPS 2019 • Moses Charikar, Kirankumar Shiragur, Aaron Sidford
In this paper we provide a general framework for estimating symmetric properties of distributions from i. i. d.
no code implementations • 6 Apr 2020 • Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford
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).
no code implementations • NeurIPS 2020 • Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford
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.
no code implementations • 4 Feb 2021 • Yeganeh Alimohammadi, Nima Anari, Kirankumar Shiragur, Thuy-Duong Vuong
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
no code implementations • 13 Oct 2022 • Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford
We provide an efficient unified plug-in approach for estimating symmetric properties of distributions given $n$ independent samples.
1 code implementation • 8 May 2023 • Davin Choo, Kirankumar Shiragur
Recovering causal relationships from data is an important problem.
1 code implementation • 9 Jun 2023 • Davin Choo, Kirankumar Shiragur
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.
no code implementations • 19 Nov 2023 • Shivam Garg, Chirag Pabbaraju, Kirankumar Shiragur, Gregory Valiant
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.
1 code implementation • NeurIPS 2023 • Kirankumar Shiragur, JiaQi Zhang, Caroline Uhler
In our work, we focus on two such well-motivated problems: subset search and causal matching.
no code implementations • 9 Mar 2024 • JiaQi Zhang, Kirankumar Shiragur, Caroline Uhler
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?