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A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Dérivation automatique

Graph Chatbot

Chattez avec Graph Search

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A first-order primal-dual method with adaptivity to local smoothness

Continual Test-Time Domain Adaptation

Efficient and Accurate Physically-Based Differentiable Rendering

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Differentiable Patch Selection for Image Recognition

A first-order primal-dual method with adaptivity to local smoothness

FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows

Large Steps in Inverse Rendering of Geometry