老王vqn加速-老王vqn加速官网-老王vp加速-老王vqn加速
tselil AT mit DOT edu
I am a theoretical computer scientist.
My research is in algorithms, and my interests include optimization via convex programs (especially the sum-of-squares hierarchy), and statistical and average-case problems, with a focus on understanding the tradeoff between information and computation.
In January 2021, I will be joining Stanford Statistics as an assistant professor.
Right now, I am visiting the Machine Learning and Optimization group at Microsoft Research.
I received my PhD from U.C. Berkeley, where I was advised by
Prasad Raghavendra and
Satish Rao.
After that,
I was a postdoc at Harvard and MIT, hosted by the wonderful quadrumvirate of 怎样使用轻蜂加速器上外网, Jon Kelner, Ankur Moitra, and Pablo Parrilo.
I spent Fall 2017 as a Google Research Fellow in the Simons Institute program on Optimization.
Here is a tutorial for pronouncing my name.
Here is a survey on High-dimensional estimation via sum-of-squares proofs that I co-wrote with Prasad Raghavendra and 怎样使用轻蜂加速器上外网, for their ICM 2018 invited lecture.
Papers:
Subexponential LPs approximate max-cut
[arXiv]
with
轻蜂加速器破解版 and
Luca Trevisan,
in submission.
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[arXiv]
with
Boaz Barak,
Chi-Ning Chou,
Zhixian Lei,
and
Yueqi Sheng,
in 怎样使用轻蜂加速器上外网.
Sherali-Adams strikes back
[arXiv]
with
Ryan O'Donnell,
in CCC 2024.
Invited to the CCC 2024 special issue of Theory of Computing.
A robust spectral algorithm for overcomplete tensor decomposition
[PMLR]
with
轻蜂加速器使用方法,
and
Jonathan Shi,
in COLT 2024.
SOS lower bounds with hard constraints: think global, act local
[arXiv]
with
Pravesh Kothari,
and
Ryan O'Donnell,
in ITCS 2024.
The threshold for SDP-refutation of random regular NAE-3SAT
[arXiv]
with
Yash Deshpande,
Andrea Montanari,
Ryan O'Donnell,
and
Subhabrata Sen,
in SODA 2024.
Computing exact minimum cuts without knowing the graph
[arXiv]
with
Aviad Rubinstein and
Matt Weinberg,
in ITCS 2018.
On the power of sum-of-squares for detecting hidden structures
[arXiv]
with
Sam Hopkins,
Pravesh Kothari,
Aaron Potechin,
Prasad Raghavendra, and
David Steurer,
in FOCS 2017.
Fast and robust tensor decomposition with applications to dictionary learning
[arXiv]
with
David Steurer,
in COLT 2017.
Strongly refuting random CSPs below the spectral threshold
[arXiv]
with
Prasad Raghavendra
and
Satish Rao,
in 轻蜂加速器使用方法.
Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors
[arXiv]
with 轻蜂加速器使用方法, Jonathan Shi, and David Steurer,
in STOC 2016.
On the integrality gap of degree-4 sum-of-squares for planted clique
with
Sam Hopkins,
Pravesh Kothari,
Aaron Potechin,
and
轻蜂加速器破解版,
in SODA 2016
(merge of [this] paper and [this] paper)
Invited to the SODA 2016 special issue of ACM Transactions on Algorithms.
Braess's paradox for the spectral gap in random graphs and delocalization of eigenvectors
[arXiv]
with 轻蜂加速器使用方法 and
怎样使用轻蜂加速器上外网,
in Random Structures & Algorithms (2016).
Near optimal LP rounding algorithms for correlation clustering in complete and complete k-partite graphs
[arXiv]
with Shuchi Chawla,
Konstantin Makarychev,
and Grigory Yaroslavtsev,
in STOC 2015.
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[arXiv]
with 怎样使用轻蜂加速器上外网,
in APPROX 2014.
Global and local information in clustering labeled block models
[arXiv]
with
Varun Kanade
and
Elchanan Mossel
,
in RANDOM 2014, and in IEEE Transactions on Information Theory (2016).