Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Suppose Alice holds a uniformly random string X ∈ {0, 1}N and Bob holds a noisy version Y of X where each bit of X is flipped independently with probability e ∈ [0, 1/2]. Alice and Bob would like to extract a common random string of min-entropy at least k. In this work,  establish the communication versus success probability trade-off for this problem by giving a protocol and a matching lor bound (under the restriction that the string to be agreed upon is determined by Alice's input X). Specifically,  prove that in order for Alice and Bob to agree on a common string with probability 2-γk (γk ≥ 1), the optimal communication (up to o(k) terms, and achievable for large N) is precisely [EQUATION], where C:= 4e(1 - e). In particular, the optimal communication to achieve Ω(1) agreement probability approaches 4e(1 - e)k.#R##N##R##N# also consider the case when Y is the output of the binary erasure channel on X, where each bit of Y equals the corresponding bit of X with probability 1 - e and is otherwise erased (that is, replaced by a '?'). In this case, the communication required becomes [EQUATION]. In particular, the optimal communication to achieve Ω(1) agreement probability approaches ek, and with no communication the optimal agreement probability approaches [EQUATION].#R##N##R##N#Our protocols are based on covering codes and extend the approach of (Bogdanov and Mossel, 2011) for the zero-communication case. Our lor bounds rely on hypercontractive inequalities. For the model of bit-flips, our argument extends the approach of (Bogdanov and Mossel, 2011) by allowing communication; for the erasure model, to the best of our knowledge the needed hypercontractivity statement was not studied before, and it was established (given our application) by (Nair and Wang 2015).  also obtain information complexity lor bounds for these tasks, and together with our protocol, they shed light on the recently popular "most informative Boolean function" conjecture of Courtade and Kumar.
['Venkatesan Guruswami', 'Jaikumar Radhakrishnan']
I am writing a research paper on a idea Tight bounds for communication-assisted agreement distillation
[{"from": "human", "value": "I am writing a research paper on a idea Tight bounds for communication-assisted agreement distillation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Suppose Alice holds a uniformly random string X \u2208 {0, 1}N and Bob holds a noisy version Y of X where each bit of X is flipped independently with probability e \u2208 [0, 1/2]. Alice and Bob would like to extract a common random string of min-entropy at least k. In this work,  establish the communication versus success probability trade-off for this problem by giving a protocol and a matching lor bound (under the restriction that the string to be agreed upon is determined by Alice's input X). Specifically,  prove that in order for Alice and Bob to agree on a common string with probability 2-\u03b3k (\u03b3k \u2265 1), the optimal communication (up to o(k) terms, and achievable for large N) is precisely [EQUATION], where C:= 4e(1 - e). In particular, the optimal communication to achieve \u03a9(1) agreement probability approaches 4e(1 - e)k.#R##N##R##N# also consider the case when Y is the output of the binary erasure channel on X, where each bit of Y equals the corresponding bit of X with probability 1 - e and is otherwise erased (that is, replaced by a '?'). In this case, the communication required becomes [EQUATION]. In particular, the optimal communication to achieve \u03a9(1) agreement probability approaches ek, and with no communication the optimal agreement probability approaches [EQUATION].#R##N##R##N#Our protocols are based on covering codes and extend the approach of (Bogdanov and Mossel, 2011) for the zero-communication case. Our lor bounds rely on hypercontractive inequalities. For the model of bit-flips, our argument extends the approach of (Bogdanov and Mossel, 2011) by allowing communication; for the erasure model, to the best of our knowledge the needed hypercontractivity statement was not studied before, and it was established (given our application) by (Nair and Wang 2015).  also obtain information complexity lor bounds for these tasks, and together with our protocol, they shed light on the recently popular \"most informative Boolean function\" conjecture of Courtade and Kumar."}]