Is randomness ever necessary for space-efficient computation? It is commonly conjectured that L = BPL, meaning that halting decision algorithms can always be derandomized without increasing their space complexity by more than a constant factor. In the past few years (say, from 2017 to 2022), there has been some exciting ... more >>>

The working conjecture from K'04 that there is a proof complexity generator hard for all
proof systems can be equivalently formulated (for p-time generators) without a reference to proof complexity notions
as follows:
\begin{itemize}
\item There exist a p-time function $g$ extending each input by one bit such that its ... more >>>

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT'90, SICOMP'91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness ... more >>>