One-Shot Learning for k-SAT
This addresses a theoretical gap in understanding the feasibility threshold for one-shot learning in k-SAT, with implications for computational learning theory and Markov random fields.
The paper tackles the problem of one-shot learning of the parameter β from a single satisfying assignment of a k-SAT formula, showing that learning is infeasible for degrees d as low as k² when β is large, and possible for d up to about 2^{k/2} when β is near zero, which is nearly optimal.
Consider a $k$-SAT formula $Φ$ where every variable appears at most $d$ times, and let $σ$ be a satisfying assignment of $Φ$ sampled proportionally to $e^{βm(σ)}$ where $m(σ)$ is the number of variables set to true and $β$ is a real parameter. Given $Φ$ and $σ$, can we learn the value of $β$ efficiently? This problem falls into a recent line of works about single-sample ("one-shot") learning of Markov random fields. The $k$-SAT setting we consider here was recently studied by Galanis, Kandiros, and Kalavasis (SODA'24) where they showed that single-sample learning is possible when roughly $d\leq 2^{k/6.45}$ and impossible when $d\geq (k+1) 2^{k-1}$. Crucially, for their impossibility results they used the existence of unsatisfiable instances which, aside from the gap in $d$, left open the question of whether the feasibility threshold for one-shot learning is dictated by the satisfiability threshold of $k$-SAT formulas of bounded degree. Our main contribution is to answer this question negatively. We show that one-shot learning for $k$-SAT is infeasible well below the satisfiability threshold; in fact, we obtain impossibility results for degrees $d$ as low as $k^2$ when $β$ is sufficiently large, and bootstrap this to small values of $β$ when $d$ scales exponentially with $k$, via a probabilistic construction. On the positive side, we simplify the analysis of the learning algorithm and obtain significantly stronger bounds on $d$ in terms of $β$. In particular, for the uniform case $β\rightarrow 0$ that has been studied extensively in the sampling literature, our analysis shows that learning is possible under the condition $d\lesssim 2^{k/2}$. This is nearly optimal (up to constant factors) in the sense that it is known that sampling a uniformly-distributed satisfying assignment is NP-hard for $d\gtrsim 2^{k/2}$.