Yaniv Plan

Associate Professor

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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2021)
Near-optimal sample complexity for noisy or 1-bit tensor completion (2018)

Tensor completion is the problem of recovering a low-rank tensor from a partial subset of its entries. Assuming a rank-r, order-d tensor in ℝ^{NxNx...N}, the best sampling complexity achieved is O(rN^{d/2}) which can be obtained by a tensor nuclear-norm minimization problem. This bound is significantly larger than O(rdN), the number of free variables in a rank-r tensor. In this thesis, we prove that when r=O(1), we can achieve optimal sample complexity by constraining either one of two proxies for tensor rank, the convex M-norm or the non-convex max-qnorm. The max-qnorm is the generalization of matrix max-norm to tensors which is non-convex. The M-norm, on the other hand, is a convex atomic norm whose atoms are rank-1 sign tensors. We prove that both max-qnorm and M-norm of a bounded rank-r tensor are bounded by quantities that are independent of N. We also prove that the unit balls of both these norms have small Rademacher complexity.We analyze max-qnorm and M-norm constrained least squares minimization problems for tensor completion, proving that when r=O(1), m=O(Nd) measurements are sufficient for efficient estimation of the original tensor. We also use an information theoretic technique to prove that the dependence on N is optimal. Moreover, we design an alternating method for approximating the solution of max-qnorm tensor completion and do a thorough investigation of its performance on synthetic and real-world data.We also generalize the 1-bit matrix completion problem to higher-order tensors. We prove that when r=O(1) a bounded rank-r, order-d tensor T in ℝ^N x ℝ^N x ... x ℝ^N can be estimated efficiently by only m=O(Nd) binary measurements by regularizing either its max-qnorm or its M-norm. We prove that the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is the same as recovering it from unquantized measurements. Moreover, we show the advantage of using 1-bit tensor completion over matricization both theoretically and numerically. Specifically, we show how the 1-bit measurement model can be used for context-aware recommender systems.

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