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- Benjamin M. Gyori, Daniel Paulin
- Statistics and Computing
- 2016

This paper establishes new concentration inequalities for random matrices constructed from independent random variables. These results are analogous with the generalized Efron–Stein inequalities developed by Boucheron et al. The proofs rely on the method of exchangeable pairs.

This paper derives exponential tail bounds and polynomial moment inequalities for the spectral norm deviation of a random matrix from its mean value. The argument depends on a matrix extension of Stein's method of exchangeable pairs for concentration of measure, as introduced by Chatterjee. Recent work of Mackey et al. uses these techniques to analyze… (More)

The construction and formal verification of dynamical models is important in engineering, biology and other disciplines. We focus on non-linear models containing a set of parameters governing their dynamics. The value of these parameters is often unknown and not directly observable through measurements, which are themselves noisy. When treating parameters… (More)

- Daniel Paulin
- 2014

J o u r n a l o f P r o b a b i l i t y Electron. Abstract We prove concentration inequalities for general functions of weakly dependent random variables satisfying the Dobrushin condition. In particular, we show Talagrand's convex distance inequality for this type of dependence. We apply our bounds to a version of the stochastic salesman problem, the… (More)

- Lester Mackey, Ameet Talwalkar, +4 authors Daniel Paulin
- 2014

The goal in matrix completion is to recover a matrix from a small subset of noisy entries. Web-scale instances of this problem include collaborative filtering for recommendation and link prediction in social networks. Many modern matrix completion algorithms provide strong recovery guarantees but scale poorly due to the repeated and costly computation of… (More)

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