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Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply implicit differentiation without the invertibility condition | Gradient descent through the fixed-point/optimization layer · Bilevel toy problem | convergence to critical points | Hurt |
| Apply implicit differentiation without the invertibility condition | Gradient ascent through the implicit optimization layer · Lorenz-derived quadratic optimization problem | gradient dynamics | Hurt |
| Slightly perturb the problem and initializations | Gradient descent through the fixed-point/optimization layer · Perturbed bilevel toy problem | persistence of limit-cycle dynamics | No clear effect |
Closest papers by meaning
- On the complexity of nonsmooth automatic differentiation (ICLR 2023 · similarity 0.90)
- Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates (ICML 2024 · similarity 0.89)
- A mathematical model for automatic differentiation in machine learning (NeurIPS 2020 · similarity 0.87)
- Automatic differentiation of nonsmooth iterative algorithms (NeurIPS 2022 · similarity 0.87)
- Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions (ICML 2024 · similarity 0.85)
- Escaping saddle points without Lipschitz smoothness: the power of nonlinear preconditioning (NeurIPS 2025 · similarity 0.85)
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