Tractability from overparametrization: the example of the negative perceptron

Probability Theory and Related Fields(2024)

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In the negative perceptron problem we are given n data points (x_i,y_i) , where x_i is a d -dimensional vector and y_i∈{+1,-1} is a binary label. The data are not linearly separable and hence we content ourselves to find a linear classifier with the largest possible negative margin. In other words, we want to find a unit norm vector θ that maximizes min _i≤ ny_i⟨θ,x_i⟩ . This is a non-convex optimization problem (it is equivalent to finding a maximum norm vector in a polytope), and we study its typical properties under two random models for the data. We consider the proportional asymptotics in which n,d→∞ with n/d→δ , and prove upper and lower bounds on the maximum margin κ _s(δ ) or—equivalently—on its inverse function δ _s(κ ) . In other words, δ _s(κ ) is the overparametrization threshold: for n/d≤δ _s(κ )-ε a classifier achieving vanishing training error exists with high probability, while for n/d≥δ _s(κ )+ε it does not. Our bounds on δ _s(κ ) match to the leading order as κ→ -∞ . We then analyze a linear programming algorithm to find a solution, and characterize the corresponding threshold δ _lin(κ ) . We observe a gap between the interpolation threshold δ _s(κ ) and the linear programming threshold δ _lin(κ ) , raising the question of the behavior of other algorithms.
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60D05 Geometric probability and stochastic geometry,68T07 Artificial neural networks and deep learning,82B44 Disordered systems (random Ising models random Schrödinger operators etc.) in equilibrium statistical mechanics
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