Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Dudeja, Rishabh"'
We study the problem of estimating a rank one signal matrix from an observed matrix generated by corrupting the signal with additive rotationally invariant noise. We develop a new class of approximate message-passing algorithms for this problem and p
Externí odkaz:
http://arxiv.org/abs/2405.18081
It has been observed that the performances of many high-dimensional estimation problems are universal with respect to underlying sensing (or design) matrices. Specifically, matrices with markedly different constructions seem to achieve identical perf
Externí odkaz:
http://arxiv.org/abs/2208.02753
Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity
Autor:
Dudeja, Rishabh, Hsu, Daniel
Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-order moment tensors. Unlike its matrix counterpart, Tensor PCA exhibits a
Externí odkaz:
http://arxiv.org/abs/2204.07526
Approximate Message Passing (AMP) is a class of iterative algorithms that have found applications in many problems in high-dimensional statistics and machine learning. In its general form, AMP can be formulated as an iterative procedure driven by a m
Externí odkaz:
http://arxiv.org/abs/2204.04281
Autor:
Dudeja, Rishabh
Phase Retrieval is an inference problem where one seeks to recover an unknown complex-valued 𝓃-dimensional signal vector from the magnitudes of 𝓶 linear measurements. The linear measurements are specified using a 𝓶 × 𝓃 sensing matrix. Th
Autor:
Dudeja, Rishabh, Bakhshizadeh, Milad
In the phase retrieval problem one seeks to recover an unknown $n$ dimensional signal vector $\mathbf{x}$ from $m$ measurements of the form $y_i = |(\mathbf{A} \mathbf{x})_i|$, where $\mathbf{A}$ denotes the sensing matrix. Many algorithms for this p
Externí odkaz:
http://arxiv.org/abs/2008.10503
Autor:
Dudeja, Rishabh, Hsu, Daniel
In the Tensor PCA problem introduced by Richard and Montanari (2014), one is given a dataset consisting of $n$ samples $\mathbf{T}_{1:n}$ of i.i.d. Gaussian tensors of order $k$ with the promise that $\mathbb{E}\mathbf{T}_1$ is a rank-1 tensor and $\
Externí odkaz:
http://arxiv.org/abs/2008.04101
We study information theoretic limits of recovering an unknown $n$ dimensional, complex signal vector $\mathbf{x}_\star$ with unit norm from $m$ magnitude-only measurements of the form $y_i = |(\mathbf{A} \mathbf{x}_\star)_i|^2, \; i = 1,2 \dots , m$
Externí odkaz:
http://arxiv.org/abs/1910.11849
Phase retrieval refers to the problem of recovering a signal $\mathbf{x}_{\star}\in\mathbb{C}^n$ from its phaseless measurements $y_i=|\mathbf{a}_i^{\mathrm{H}}\mathbf{x}_{\star}|$, where $\{\mathbf{a}_i\}_{i=1}^m$ are the measurement vectors. Many p
Externí odkaz:
http://arxiv.org/abs/1903.02505
Phase retrieval refers to algorithmic methods for recovering a signal from its phaseless measurements. Local search algorithms that work directly on the non-convex formulation of the problem have been very popular recently. Due to the nonconvexity of
Externí odkaz:
http://arxiv.org/abs/1903.02676