Optimization
Maximum Causal Tsallis Entropy Imitation Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
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Riemannian stochastic optimization methods avoid strict saddle points Anonymous Author(s) Affiliation Address email
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Riemannian setting is a highly challenging affair.
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The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Shuang Li, Gongguo Tang, Michael B. Wakin
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AQuantitativeGeometricApproachto Neural-NetworkSmoothness
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The techniques for theℓ2-perturbation do not easily transfer to theℓ -case (Leino et al., 2021). One critical step is to measure the Lipschitz constant more precisely and efficiently.
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