Time efficient decoding algorithms for error correcting codes often require linear space. However, locally decodable codes yield more efficient randomized decoders that run in time $n^{1+o(1)}$ and space $n^{o(1)}$. In this work we focus on deterministic decoding.
Gronemeier showed that any non-adaptive deterministic decoder for a good code running ... more >>>

We give the first explicit constant rate, constant relative distance, linear codes with an encoder that runs in time $n^{1 + o(1)}$ and space $\mathop{polylog}(n)$ provided random access to the message. Prior to this work, the only such codes were non-explicit, for instance repeat accumulate codes [DJM98] and the codes ... more >>>

The celebrated $\mathbf{IP}=\mathbf{PSPACE}$ Theorem gives an efficient interactive proof for any bounded-space algorithm. In this work we study interactive proofs for non-deterministic bounded space computations. While Savitch's Theorem shows that nondeterministic bounded-space algorithms can be simulated by deterministic bounded-space algorithms, this simulation has a quadratic overhead. We give interactive protocols ... more >>>

Let $\mathbf{TISP}[T, S]$, $\mathbf{BPTISP}[T, S]$, $\mathbf{NTISP}[T, S]$, and $\mathbf{CoNTISP}[T, S]$ be the set of languages recognized by deterministic, randomized, nondeterminsitic, and co-nondeterministic algorithms, respectively, running in time $T$ and space $S$. Let $\mathbf{ITIME}[T_V]$ be the set of languages recognized by an interactive protocol where the verifier runs in time $T_V$. ... more >>>

We prove for some constant $a > 1$, for all $k \leq a$,
$$\mathbf{MATIME}[n^{k + o(1)}] / 1 \not \subset \mathbf{SIZE}[O(n^{k})],$$
for some specific $o(1)$ function. This improves on the Santhanam lower bound, which says there exists constant $c$ such that for all $k > 1$:
$$\mathbf{MATIME}[n^{c k}] / 1 ... more >>>

We give two results on the size of AC0 circuits computing promise majority. $\epsilon$-promise majority is majority promised that either at most an $\epsilon$ fraction of the input bits are 1, or at most $\epsilon$ are 0.

First, we show super quadratic lower bounds on both monotone and general depth ... more >>>