Prove MemoryPrecision (openai/math family 140: Memory–sample lower bounds for noiseless Gaussian regression)…
Prove MemoryPrecision (openai/math family 140: Memory–sample lower bounds for noiseless Gaussian regression)…: a task in openai/math as Harbor environments (Harbor dataset). Context: this statement belongs to family 140 of the release, Memory–sample lower bounds for noiseless Gaussian regression…
The task
Context: this statement belongs to family 140 of the release, *Memory–sample lower bounds for noiseless Gaussian regression* (Theoretical computer science). The family as a whole: For fixed $A>0$, a one-pass learner with $Ad^2$ persistent bits needs $\Omega_A(d\log(1/\epsilon))$ noiseless Gaussian samples to recover…
Part of raising-heart/openai-math.