Tselil Schramm's papers (by topic):

Information-computation tradeoffs:

Semidefinite programs simulate approximate message passing robustly [arXiv]
with Misha Ivkov, in STOC 2024.
The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics [arXiv]
with Afonso Bandeira, Ahmed El Alaoui, Sam Hopkins, Alex Wein, and Ilias Zadik, in NeurIPS 2022.

The SDP value of random 2CSPs [arXiv]
with Amulya Musipatla, Ryan O'Donnell, and Xinyu Wu, in ICALP 2022.

Statistical query algorithms and low-degree tests are almost equivalent [arXiv]
with Matthew Brennan, Guy Bresler, Sam Hopkins and Jerry Li, in COLT 2021, runner up to the Best Paper.

Computational barriers to estimation from low-degree polynomials [arXiv]
with Alex Wein, in The Annals of Statistics, 2022.

The strongish planted clique hypothesis and its consequences [arXiv]
with Pasin Manurangsi and Aviad Rubinstein, in ITCS 2021.

SOS lower bounds with hard constraints: think global, act local [arXiv]
with Pravesh Kothari, and Ryan O'Donnell, in ITCS 2019.

The threshold for SDP-refutation of random regular NAE-3SAT [arXiv]
with Yash Deshpande, Andrea Montanari, Ryan O'Donnell, and Subhabrata Sen, in SODA 2019.

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.

On the integrality gap of degree-4 sum-of-squares for planted clique
with Sam Hopkins, Pravesh Kothari, Aaron Potechin, and Prasad Raghavendra, in SODA 2016 (merge of [this] paper and [this] paper)
Invited to the SODA 2016 special issue of ACM Transactions on Algorithms.

Algorithms for estimation, hypothesis testing, and refutation:

Robust Regression Revisited: Acceleration and Improved Estimation Rates [arXiv]
with Arun Jambulapati, Jerry Li, and Kevin Tian, in NeurIPS 2021.

(Nearly) efficient algorithms for the graph matching problem on correlated random graphs
with Boaz Barak, Chi-Ning Chou, Zhixian Lei, and Yueqi Sheng, in NeurIPS 2019.

A robust spectral algorithm for overcomplete tensor decomposition [PMLR]
with Sam Hopkins, and Jonathan Shi, in COLT 2019.
Spectral clustering in the Gaussian mixture block model [arXiv]
with Shuangping Li , in submission.

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 STOC 2017.

Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors [arXiv]
with Sam Hopkins, Jonathan Shi, and David Steurer, in STOC 2016.

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).

Random Graphs:

Local and global expansion in random geometric graphs [arXiv]
with Siqi Liu, Sidhanth Mohanty, and Elizabeth Yang, in STOC 2023.

Testing thresholds for high-dimensional sparse random geometric graphs [arXiv]
with Siqi Liu, Sidhanth Mohanty, and Elizabeth Yang, in STOC 2022.
Invited to the STOC 2022 special issue of SICOMP.

Braess's paradox for the spectral gap in random graphs and delocalization of eigenvectors [arXiv]
with Ronen Eldan and Miklos Racz, in Random Structures & Algorithms (2016).

Approximation Algorithms and Convex Programs:

Playing unique games on certified small-set expanders [arXiv]
with Mitali Bafna, Boaz Barak, Pravesh Kothari and David Steurer, in STOC 2021.

Subexponential LPs approximate max-cut [arXiv]
with Sam Hopkins and Luca Trevisan, in FOCS 2020.

Sherali-Adams strikes back [arXiv]
with Ryan O'Donnell, in CCC 2019.
Invited to the CCC 2019 special issue of Theory of Computing.

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.

Gap amplification for small-set expansion via random walks [arXiv]
with Prasad Raghavendra, in APPROX 2014.


Non-asymptotic approximations of neural networks by Gaussian processes [arXiv]
with Dan Mikulincer and Ronen Eldan, in COLT 2021.

Computing exact minimum cuts without knowing the graph [arXiv]
with Aviad Rubinstein and Matt Weinberg, in ITCS 2018.