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Samira Goudarzi

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7 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Dynamic Diameter in High-Dimensions against Adaptive Adversary and Beyond

Kiarash Banihashem
Jeff Giliberti
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

In this paper, we study the fundamental problems of maintaining the diameter and a $k$-center clustering of a dynamic point set $P \subset \mathbb{R}^d$, where points may be inserted or deleted over time and the ambient dimension $d$ is not constant and may be high. Our focus is on designing algorithms that remain effective even in the presence of an \emph{adaptive adversary}—an adversary that, at any time $t$, knows the entire history of the algorithm’s outputs as well as all the random bits used by the algorithm up to that point. We present a fully dynamic algorithm that maintains a $2$-approximate diameter with a \emph{worst-case} update time of $poly(d, \log n)$, where $n$ is the length of the stream. Our result is achieved by identifying a robust representative of the dataset that requires infrequent updates, combined with a careful deamortization. To the best of our knowledge, this is the first efficient fully-dynamic algorithm for diameter in high dimensions that \emph{simultaneously} achieves a $2$-approximation guarantee and robustness against an adaptive adversary. We also give an improved dynamic $(4+\epsilon)$-approximation algorithm for the $k$-center problem, also resilient to an adaptive adversary. Our clustering algorithm achieves an amortized update time of $k^{2. 5} d \cdot poly(\epsilon^{-1}, \log n)$, improving upon the amortized update time of $k^6 d \cdot poly( \epsilon^{-1}, \log n)$ by Biabani et al. [NeurIPS'24].

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NeurIPS Conference 2025 Conference Paper

Non-monotone Submodular Optimization: $p$-Matchoid Constraints and Fully Dynamic Setting

Kiarash Banihashem
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

Submodular maximization subject to a $p$-matchoid constraint has various applications in machine learning, particularly in tasks such as feature selection, video and text summarization, movie recommendation, graph-based learning, and constraint-based optimization. We study this problem in the dynamic setting, where a sequence of insertions and deletions of elements to a $p$-matchoid $\mathcal{M}(\mathcal{V}, \mathcal{I})$ occurs over time and the goal is to efficiently maintain an approximate solution. We propose a dynamic algorithm for non-monotone submodular maximization under a $p$-matchoid constraint. For a $p$-matchoid $\mathcal{M}(\mathcal{V}, \mathcal{I})$ of rank $k$, defined by a collection of $m$ matroids, our algorithm guarantees a $(2p + 2\sqrt{p(p+1)} + 1 + \epsilon)$-approximate solution at any time $t$ in the update sequence, with an expected amortized query complexity of $O(\epsilon^{-3} pk^4 \log^2(k))$ per update.

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NeurIPS Conference 2025 Conference Paper

Replicable Online pricing

Kiarash Banihashem
MohammadHossein Bateni
Hossein Esfandiari
Samira Goudarzi
MohammadTaghi Hajiaghayi

We explore the concept of replicability, which ensures algorithmic consistency despite input data variations, for online pricing problems, specifically prophet inequalities and delegation. Given the crucial role of replicability in enhancing transparency in economic decision-making, we present a replicable and nearly optimal pricing strategy for prophet inequalities, achieving a sample complexity of $\textnormal{poly}(\log^* |\mathcal{X}|)$, where $\mathcal{X}$ is the ground set of distributions. Furthermore, we extend these findings to the delegation problem and establish lower bound that proves the necessity of the $\log^*|\mathcal{X}|$ dependence. En route to obtaining these results, we develop a number of technical contributions which are of independent interest. Most notably, we propose a new algorithm for a variant of the heavy hitter problem, which has a nearly linear dependence on the inverse of the heavy hitter parameter, significantly improving upon existing results which have a cubic dependence.

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ICML Conference 2024 Conference Paper

A Dynamic Algorithm for Weighted Submodular Cover Problem

Kiarash Banihashem
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

We initiate the study of the submodular cover problem in a dynamic setting where the elements of the ground set are inserted and deleted. In the classical submodular cover problem, we are given a monotone submodular function $f: 2^{V} \to \mathbb{R}^{\ge 0}$ and the goal is to obtain a set $S \subseteq V$ that minimizes the cost subject to the constraint $f(S) = f(V)$. This is a classical problem in computer science and generalizes the Set Cover problem, 2-Set Cover, and dominating set problem among others. We consider this problem in a dynamic setting where there are updates to our set $V$, in the form of insertions and deletions of elements from a ground set $\mathcal{V}$, and the goal is to maintain an approximately optimal solution with low query complexity per update. For this problem, we propose a randomized algorithm that, in expectation, obtains a $(1-O(\epsilon), O(\epsilon^{-1}))$-bicriteria approximation using polylogarithmic query complexity per update.

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SODA Conference 2024 Conference Paper

Dynamic Algorithms for Matroid Submodular Maximization

Kiarash Banihashem
Leyla Biabani
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

Submodular maximization under matroid and cardinality constraints are classical problems with a wide range of applications in machine learning, auction theory, and combinatorial optimization. In this paper, we consider these problems in the dynamic setting where (1) we have oracle access to a monotone submodular function f: 2 V → ℝ + and (2) we are given a sequence S of insertions and deletions of elements of an underlying ground set V. We develop the first fully dynamic algorithm for the submodular maximization problem under the matroid constraint that maintains a (4 + ɛ )-approximation solution (0 < ɛ ≤ 1) using an expected query complexity of O(k log( k ) log 3 (k/ɛ)), which is indeed parameterized by the rank k of the matroid M(V, I) as well. Chen and Peng [52] at STOC’22 studied the complexity of this problem in the insertion-only dynamic model (a restricted version of the fully dynamic model where deletion is not allowed), and they raised the following important open question: “for fully dynamic streams [sequences of insertions and deletions of elements], there is no known constant-factor approximation algorithm with poly(k) amortized queries for matroid constraints. ” Our dynamic algorithm answers this question as well as an open problem of Lattanzi et al. [109] (NeurIPS’20) affirmatively. As a byproduct, for the submodular maximization under the cardinality constraint k, we propose a parameterized (by the cardinality constraint k) dynamic algorithm that maintains a (2 + ɛ )-approximate solution of the sequence S at any time t using an expected query complexity of O(kɛ -1 log 2 ( k )), which is an improvement upon the dynamic algorithm that Monemizadeh [125] (NeurIPS’20) developed for this problem using an expected query complexity O (k 2 ɛ -3 log 5 ( n )). In particular, this dynamic algorithm is the first one for this problem whose query complexity is independent of the size of ground set V (i. e. , n = | V |). We develop our dynamic algorithm for the submodular maximization problem under the matroid or cardinality constraint by designing a randomized leveled data structure that supports insertion and deletion operations, maintaining an approximate solution for the given problem. In addition, we develop a fast construction algorithm for our data structure that uses a one-pass over a random permutation of the elements and utilizes monotonicity property of our problems which has a subtle proof in the matroid case. We believe these techniques could also be useful for other optimization problems in the area of dynamic algorithms.

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ICML Conference 2023 Conference Paper

Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time

Kiarash Banihashem
Leyla Biabani
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

Maximizing a monotone submodular function under cardinality constraint $k$ is a core problem in machine learning and database with many basic applications, including video and data summarization, recommendation systems, feature extraction, exemplar clustering, and coverage problems. We study this classic problem in the fully dynamic model where a stream of insertions and deletions of elements of an underlying ground set is given and the goal is to maintain an approximate solution using a fast update time. A recent paper at NeurIPS’20 by Lattanzi, Mitrovic, Norouzi-Fard, Tarnawski, Zadimoghaddam claims to obtain a dynamic algorithm for this problem with a $(\frac{1}{2} -\epsilon)$ approximation ratio and a query complexity bounded by $\mathrm{poly}(\log(n), \log(k), \epsilon^{-1})$. However, as we explain in this paper, the analysis has some important gaps. Having a dynamic algorithm for the problem with polylogarithmic update time is even more important in light of a recent result by Chen and Peng at STOC’22 who show a matching lower bound for the problem – any randomized algorithm with a $\frac{1}{2}+\epsilon$ approximation ratio must have an amortized query complexity that is polynomial in $n$. In this paper, we develop a simpler algorithm for the problem that maintains a $(\frac{1}{2}-\epsilon)$-approximate solution for submodular maximization under cardinality constraint $k$ using a polylogarithmic amortized update time.

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NeurIPS Conference 2023 Conference Paper

Dynamic Non-monotone Submodular Maximization

Kiarash Banihashem
Leyla Biabani
Samira Goudarzi
MohammadTaghi Hajiaghayi
Peyman Jabbarzade
Morteza Monemizadeh

Maximizing submodular functions has been increasingly used in many applications of machine learning, such as data summarization, recommendation systems, and feature selection. Moreover, there has been a growing interest in both submodular maximization and dynamic algorithms. In 2020, Monemizadeh and Lattanzi, Mitrovic, Norouzi-Fard, Tarnawski, and Zadimoghaddam initiated developing dynamic algorithms for the monotone submodular maximization problem under the cardinality constraint $k$. In 2022, Chen and Peng studied the complexity of this problem and raised an important open question: "\emph{Can we extend [fully dynamic] results (algorithm or hardness) to non-monotone submodular maximization? }". We affirmatively answer their question by demonstrating a reduction from maximizing a non-monotone submodular function under the cardinality constraint $k$ to maximizing a monotone submodular function under the same constraint. Through this reduction, we obtain the first dynamic algorithms to solve the non-monotone submodular maximization problem under the cardinality constraint $k$. Our algorithms maintain an $(8+\epsilon)$-approximate of the solution and use expected amortized $O(\epsilon^{-3}k^3\log^3(n)\log(k))$ or $O(\epsilon^{-1}k^2\log^3(k))$ oracle queries per update, respectively. Furthermore, we showcase the benefits of our dynamic algorithm for video summarization and max-cut problems on several real-world data sets.

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