Oren Mangoubi

activity

20152025

most citedRapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions

70 citations · 138 across the 21 of their papers we have counts for

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12 papers · 1 filter

cs.DS2025

Private Low-Rank Approximation for Covariance Matrices, Dyson Brownian Motion, and Eigenvalue-Gap Bounds for Gaussian Perturbations

Oren Mangoubi, Nisheeth K. Vishnoi

We consider the problem of approximating a $d \times d$ covariance matrix $M$ with a rank- $k$ matrix under $(ε, δ)$ -differential privacy. We present and analyze a complex…

cs.DS2024

Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers

Oren Mangoubi, Nisheeth K. Vishnoi

We consider the problem of sampling from a log-concave distribution $π (θ) \propto e^{- f (θ)}$ constrained to a polytope $K := {θ \in R^{d} : A θ \leq b}$ , where $A\in \mathbb{R}^…

cs.DS2023

Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations

Oren Mangoubi, Nisheeth K. Vishnoi

We consider the problem of approximating a $d \times d$ covariance matrix $M$ with a rank- $k$ matrix under $(ε, δ)$ -differential privacy. We present and analyze a complex…

cs.DS2022★ 1 cited

Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion

Oren Mangoubi, Nisheeth K. Vishnoi

Given a symmetric matrix $M$ and a vector $λ$ , we present new bounds on the Frobenius-distance utility of the Gaussian mechanism for approximating $M$ by a matrix whose spectrum is…

cs.DS2022

Private Matrix Approximation and Geometry of Unitary Orbits

Oren Mangoubi, Yikai Wu, Satyen Kale +2

Consider the following optimization problem: Given $n \times n$ matrices $A$ and $Λ$ , maximize $⟨ A, U Λ U^{*} ⟩$ where $U$ varies over the unitary group $U (n)$ . Th…

cs.DS2022

Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk

Oren Mangoubi, Nisheeth K. Vishnoi

Given a Lipschitz or smooth convex function $f : K \to R$ for a bounded polytope $K \subseteq R^{d}$ defined by $m$ inequalities, we consider the problem of sampli…

cs.DS2021

Sampling from Log-Concave Distributions with Infinity-Distance Guarantees

Oren Mangoubi, Nisheeth K. Vishnoi

For a $d$ -dimensional log-concave distribution $π (θ) \propto e^{- f (θ)}$ constrained to a convex body $K$ , the problem of outputting samples from a distribution $ν$ which is $\varep…

cs.DS2020

Greedy Adversarial Equilibrium: An Efficient Alternative to Nonconvex-Nonconcave Min-Max Optimization

Oren Mangoubi, Nisheeth K. Vishnoi

Min-max optimization of an objective function $f : R^{d} \times R^{d} \to R$ is an important model for robustness in an adversarial setting, with appl…

cs.DS2019★ 4 cited

Faster polytope rounding, sampling, and volume computation via a sublinear "Ball Walk"

Oren Mangoubi, Nisheeth K. Vishnoi

We study the problem of "isotropically rounding" a polytope $K \subset R^{n}$ , that is, computing a linear transformation which makes the uniform distribution on the polytope…

cs.DS2019★ 7 cited

Nonconvex sampling with the Metropolis-adjusted Langevin algorithm

Oren Mangoubi, Nisheeth K. Vishnoi

The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applica…

cs.DS2018★ 25 cited

Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo

Oren Mangoubi, Nisheeth K. Vishnoi

Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in…

cs.DS2017★ 2 cited

Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing

Oren Mangoubi, Nisheeth K. Vishnoi

We consider the problem of minimizing a convex objective function $F$ when one can only evaluate its noisy approximation $F^$ . Unless one assumes some structure on the noise,…