We construct an explicit distribution $\mathbf{D}$ over $\{0,1\}^N$ that exhibits an essentially optimal separation between adaptive and non-adaptive cell-probe sampling. The distribution can be sampled exactly when each output bit is allowed two adaptive probes to an arbitrarily long sequence of independent uniform symbols from $[N]$. In contrast, any non-adaptive ... more >>>

List-decoding and list recovery ask how much corruption or uncertainty a code can tolerate while still keeping the number of plausible codewords small. For large alphabet codes, the ultimate benchmark for list-decoding is the ($\epsilon$-relaxed) generalized Singleton bound, which targets list-of-$L$ decoding radius with rate $R$ up to radius $\frac{L}{L+1}(1-R-\epsilon)$. ... more >>>

A famous theorem of Kruskal gives the simplest and arguably most fundamental criterion under which a tensor is guaranteed a unique minimum-rank decomposition. Kruskal's condition requires that the sum of the Kruskal ranks $\{k_i\}_{i=1}^m$ of the components satisfies $\sum_{i \in [m]} k_i \ge 2r + m - 1$, where $r$ ... more >>>