We give a simple combinatorial proof of the Chernoff-Hoeffding concentration bound~\cite{Chernoff, Hof63}, which says that the sum of independent $\{0,1\}$-valued random variables is highly concentrated around the expected value. Unlike the standard proofs,
our proof does not use the method of higher moments, but rather uses a simple ... more >>>

Matrix concentration for Markov chains was initiated in the expander-walk setting by Garg, Lee, Song, and Srivastava'18 [GLSS18]. However, the constant obtained in [GLSS18] is quite loose, and it is natural to ask whether a tighter proof can yield the same constant as in the independent matrix concentration setting. In ... more >>>