We examine the communication required for generating random variables
remotely. One party Alice will be given a distribution D, and she
has to send a message to Bob, who is then required to generate a
value with distribution exactly D. Alice and Bob are allowed
to share random bits generated ... more >>>

We study the interactive channel capacity of an $\epsilon$-noisy channel. The interactive channel capacity $C(\epsilon)$ is defined as the minimal ratio between the communication complexity of a problem (over a non-noisy channel), and the communication complexity of the same problem over the binary symmetric channel with noise rate $\epsilon$, where ... more >>>

We devise a deterministic interactive coding scheme with rate $1-O(\sqrt{\varepsilon\log(1/\varepsilon)})$ against $\varepsilon$-fraction of adversarial errors. The rate we obtain is tight by a result of Kol and Raz (STOC 2013). Prior to this work, deterministic coding schemes for any constant fraction $\varepsilon>0$ of adversarial errors could obtain rate no larger ... more >>>

Guruswami and Smith (J. ACM 2016) considered codes for channels that are poly-size circuits which modify at most a $p$-fraction of the bits of the codeword. This class of channels is significantly stronger than Shannon's binary symmetric channel (BSC), but weaker than Hamming's channels which are computationally unbounded.
Guruswami and ... more >>>

Kol and Raz [STOC 2013] showed how to simulate any alternating two-party communication protocol designed to work over the noiseless channel, by a protocol that works over a stochastic channel that corrupts each sent symbol with probability $\epsilon>0$ independently, with only a $1+\mathcal{O}(\sqrt{\H(\epsilon)})$ blowup to the communication. In particular, this ... more >>>