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STOC: Annual ACM Symposium on the Theory of Computing

STOC: Annual ACM Symposium on the Theory of Computing. Ivan Joveti ć. Conference summary. 49 th edition June 19 th to June 23 rd , 2017 in Montreal, Canada 103 papers accepted and presented, as well as 8 invited paper talks

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STOC: Annual ACM Symposium on the Theory of Computing

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  1. STOC: Annual ACM Symposium on the Theory of Computing Ivan Jovetić

  2. Conference summary • 49th edition • June 19th to June 23rd, 2017 in Montreal, Canada • 103 papers accepted and presented, as well as 8 invited paper talks • Typical topics of interest for STOC papers: optimization problems, approximation algorithms, machine learning etc.

  3. Finding Approximate Local MinimaFaster than Gradient Descent • FastCubic algorithm • FastCubic finds approximate local minima faster than first-order methods despite them only finding critical points • Applies to non-convex objectives arising in machine learning, e.g. training a neural network

  4. Katyusha: The First Direct Acceleration of Stochastic Gradient Methods • In large-scale machine learning the number of data examples is very large • stochastic gradient iterations • Stochastic gradient methods are used because they only need one example per iteration to form an estimator of the full gradient • Nesterov’s momentum trick doesn’t necessarily accelerate methods in a stochastic setting • Katyusha is a direct, primal-only stochastic gradient method that uses “negative momentum” to fix the issue

  5. Trace Reconstruction with Samples • In trace reconstruction problem, the goal is to reconstruct an unknown bit string x from multiple noisy observations of x • Focused on the case where the noise is due to x going through the deletion channel • Deletion channel deletes each bit with probability q, resulting in contracted x̃ • How many independent copies of x̃ are needed to reconstruct original x with high probability?

  6. References • Zeyuan Allen-Zhu. 2017. Katyusha: The First Direct Acceleration of Stochastic Gradient Methods. InProceedings of 49th Annual ACM SIGACT Symposium on the Theory of Computing, Montreal, Canada, June 2017 (STOC’17). DOI: 10.1145/3055399.305544 • NamanAgarwal, Zeyuan Allen-Zhu, Brian Bullins, EladHazan, and TengyuMa. 2017. Finding Approximate Local MinimaFaster than Gradient Descent. InProceedings of 49th Annual ACM SIGACTSymposium on the Theory of Computing, Montreal, Canada, June 2017 (STOC’17). DOI: 10.1145/3055399.305546 • FedorNazarov and Yuval Peres. 2017. Trace Reconstruction with Samples. In Proceedings of 49th Annual ACM SIGACT Symposium on the Theory of Computing, Montreal, Canada, June 2017 (STOC’17). DOI: 10.1145/3055399.305549

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