#### Filter Results:

- Full text PDF available (17)

#### Publication Year

1995

2017

- This year (5)
- Last 5 years (15)
- Last 10 years (17)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Method

Learn More

- Astrid Cruaud, Nina RÃ¸nsted, +26 authors Vincent Savolainen
- Systematic biology
- 2012

It is thought that speciation in phytophagous insects is often due to colonization of novel host plants, because radiations of plant and insect lineages are typically asynchronous. Recent phylogenetic comparisons have supported this model of diversification for both insect herbivores and specialized pollinators. An exceptional case where contemporaneousâ€¦ (More)

We give new rounding schemes for the standard linear programming relaxation of the correlation clustering problem, achieving approximation factors almost matching the integrality gaps: For complete graphs our approximation is 2.06 - ε, which almost matches the previously known integrality gap of 2. For complete k-partite graphs our approximation is 3.â€¦ (More)

- Varun Kanade, Elchanan Mossel, Tselil Schramm
- IEEE Transactions on Information Theory
- 2014

The stochastic block model is a classical cluster exhibiting random graph model that has been widely studied in statistics, physics, and computer science. In its simplest form, the model is a random graph with two equal-sized clusters, with intracluster edge probability p, and intercluster edge probability q. We focus on the sparse case, i.e., p, q =â€¦ (More)

- Prasad Raghavendra, Tselil Schramm
- APPROX-RANDOM
- 2014

In this work, we achieve gap amplification for the Small-Set Expansion problem. Specifically, we show that an instance of the Small-Set Expansion Problem with completeness Îµ and soundness 12 is at least as difficult as Small-Set Expansion with completeness Îµ and soundness f (Îµ), for any function f (Îµ) which grows faster than âˆš Îµ. We achieve thisâ€¦ (More)

- Samuel B. Hopkins, Tselil Schramm, Jonathan Shi, David Steurer
- STOC
- 2016

We consider two problems that arise in machine learning applications: the problem of recovering a planted sparse vector in a random linear subspace and the problem of decomposing a random low-rank overcomplete 3-tensor. For both problems, the best known guarantees are based on the sum-of-squares method. We develop new algorithms inspired by analyses of theâ€¦ (More)

- Prasad Raghavendra, Satish Rao, Tselil Schramm
- STOC
- 2017

Random constraint satisfaction problems (CSPs) are known to exhibit threshold phenomena: given a uniformly random instance of a CSP with <i>n</i> variables and <i>m</i> clauses, there is a value of <i>m</i> = Î©(<i>n</i>) beyond which the CSP will be unsatisfiable with high probability. Strong refutation is the problem of certifying that noâ€¦ (More)

- Prasad Raghavendra, Tselil Schramm
- ArXiv
- 2015

We give a lower bound of Î©Ìƒ( âˆš n) for the degree-4 Sum-of-Squares SDP relaxation for the planted clique problem. Specifically, we show that on an ErdÃ¶s-RÃ©nyi graph G(n, 1 2 ), with high probability there is a feasible point for the degree-4 SOS relaxation of the clique problem with an objective value of Î©Ìƒ( âˆš n), so that the program cannot distinguishâ€¦ (More)

- Tselil Schramm, David Steurer
- COLT
- 2017

We develop fast spectral algorithms for tensor decomposition that match the robustness guarantees of the best known polynomial-time algorithms for this problem based on the sum-of-squares (SOS) semidefinite programming hierarchy. Our algorithms can decompose a 4-tensor with n-dimensional orthonormal components in the presence of error with constant spectralâ€¦ (More)

- Samuel B. Hopkins, Pravesh Kothari, Aaron Potechin, Prasad Raghavendra, Tselil Schramm, David Steurer
- 2017 IEEE 58th Annual Symposium on Foundations ofâ€¦
- 2017

We study planted problems&#x2014;finding hidden structures in random noisy inputs&#x2014;through the lens of the sum-of-squares semidefinite programming hierarchy (SoS). This family of powerful semidefinite programs has recently yielded many new algorithms for planted problems, often achieving the best known polynomial-time guarantees in terms ofâ€¦ (More)