Most linear experimental design problems assume homogeneous variance, while the presence of heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors 𝒳⊂ℝ^d that can be probed to receive noisy linear responses of the form y=x^⊤θ^∗+η. Here θ^∗∈ℝ^d is an unknown parameter vector, and η is independent mean-zero σ_x^2-sub-Gaussian noise defined by a flexible heteroskedastic variance model, σ_x^2 = x^⊤Σ^∗x. Assuming that Σ^∗∈ℝ^d× d i…