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Consider the following classical network design model. There are n clients in a multi-graph with a single sink node. Each edge has a cost to buy, and a length if bought; typically, costlier edges…
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Kamesh Munagala Date
November 8th, 2021
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Seemingly counter-intuitive phenomena in deep neural networks and kernel methods have prompted a recent re-investigation of classical machine learning methods, like linear models. Of particular focus…
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Vidya Muthukumar Date
October 25th, 2021
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For an undirected graph with edge weights, a k-cut is a set of edges whose deletion breaks the graph into at least k connected components. How fast can we find a minimum-weight k-cut? And how many…
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Anupam Gupta Date
October 18th, 2021
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The maximum flow problem is an incredibly well-studied problem in combinatorial optimization. The problem encompasses a range of cut, matching, and scheduling problems and is a key a proving ground…
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Aaron Sidford Date
October 4th, 2021
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One of the most fundamental problems in learning theory is to view input data as random samples from an unknown distribution and then to make statistical inferences about the underlying distribution.…
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Maryam Aliakbarpour Date
March 2nd, 2020
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Local spectral expansion is a very useful method for arguing about the spectral properties of several random walk matrices over simplicial complexes. The motivation of this work is to extend this…
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Vedat Levi Alev Date
February 10th, 2020
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We say a probability distribution µ is spectrally independent if an associated correlation matrix has a bounded largest eigenvalue for the distribution and all of its conditional distributions.…
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Kuikui Liu Date
January 27th, 2020
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Robust mean estimation is the following basic estimation question: given i.i.d. copies of a random vector X in d-dimensional Euclidean space of which a small constant fraction are corrupted, how well…
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Samuel Hopkins Date
December 2nd, 2019
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In the 'frequent items' problem one sees a sequence of items in a stream (e.g. a stream of words coming into a search query engine likeGoogle) and wants to report a small list of items…
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Jelani Nelson Date
September 16th, 2019
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This talk focuses on network design algorithms for optimizing average consensus dynamics, dynamics that are widely used for information diffusion and distributed coordination in networked control…
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Yuhao Yi Date
November 18th, 2019
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(This is Part 2, continuation of Tuesday's lecture.)A fundamental tool used in sampling, counting, and inference problems is the Markov Chain Monte Carlo method, which uses random walks to solve…
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Nima Anari Date
November 6th, 2019
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A fundamental tool used in sampling, counting, and inference problems is the Markov Chain Monte Carlo method, which uses random walks to solve computational problems. The main parameter defining the…
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Nima Anari Date
November 5th, 2019
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In this talk we consider random utility models for discrete choice. In discrete choice, the task is to select exactly one element from a discrete set of alternatives. We focus on algorithmic…
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Ravi Kumar Date
November 4th, 2019
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Optimizing neural networks is a highly nonconvex problem, and even optimizing a 2-layer neural network can be challenging. In the recent years many different approaches were proposed to learn 2-layer…
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Rong Ge Date
October 28th, 2019
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In partial function extension, we are given a partial function consisting of points from a domain and a function value at each point.Our objective is to determine if this partial function can be…
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Umang Bhaskar Date
October 18th, 2019
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The Matrix Spencer Conjecture asks whether given n symmetric matrices in ℝn×n with eigenvalues in [−1,1] one can always find signs so that their signed sum has singular values bounded by…
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Thomas Rothvoss Date
October 7th, 2019
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