
How and When Random Feedback Works: A Case Study of LowRank Matrix Factorization
The success of gradient descent in ML and especially for learning neural...
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Provable Lifelong Learning of Representations
In lifelong learning, the tasks (or classes) to be learned arrive sequen...
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Assemblies of neurons can learn to classify wellseparated distributions
Assemblies are patterns of coordinated firing across large populations o...
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The Mirror Langevin Algorithm Converges with Vanishing Bias
The technique of modifying the geometry of a problem from Euclidean to H...
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Sparse Regression Faster than d^ω
The current complexity of regression is nearly linear in the complexity ...
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Tutorial on the Robust Interior Point Method
We give a short, selfcontained proof of the interior point method and i...
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Robustly Learning Mixtures of k Arbitrary Gaussians
We give a polynomialtime algorithm for the problem of robustly estimati...
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Reducing Isotropy and Volume to KLS: An O(n^3ψ^2) Volume Algorithm
We show that the the volume of a convex body in ℝ^n in the general membe...
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The Communication Complexity of Optimization
We consider the communication complexity of a number of distributed opti...
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Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions
We study Hamiltonian Monte Carlo (HMC) for sampling from a strongly logc...
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Rapid Convergence of the Unadjusted Langevin Algorithm: LogSobolev Suffices
We prove a convergence guarantee on the unadjusted Langevin algorithm fo...
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Algorithmic Theory of ODEs and Sampling from Wellconditioned Logconcave Densities
Sampling logconcave functions arising in statistics and machine learning...
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The KannanLovászSimonovits Conjecture
The KannanLovászSimonovits conjecture says that the Cheeger constant o...
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Approximating Sparse Graphs: The Random Overlapping Communities Model
How can we approximate sparse graphs and sequences of sparse graphs (wit...
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Convergence Rate of Riemannian Hamiltonian Monte Carlo and Faster Polytope Volume Computation
We give the first rigorous proof of the convergence of Riemannian Hamilt...
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Efficient Convex Optimization with Membership Oracles
We consider the problem of minimizing a convex function over a convex se...
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Unsupervised Learning through Prediction in a Model of Cortex
We propose a primitive called PJOIN, for "predictive join," which combin...
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Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
We present a simple, general technique for reducing the sample complexit...
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Santosh S. Vempala
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