By replacing the inner optimizer in RAPP we rediscover the family of Lookahead algorithms for which we establish convergence in cohypomonotone problems even when the base optimizer is taken to be gradient descent ascent.

This work was completed while Xin Y uan was a PhD student at the University of Chicago.

Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise before classification.

In this paper, we propose a provably efficient natural policy gradient algorithm called Spectral Dynamic Embedding Policy Optimization ( SDEPO) for two-player zero-sum stochastic Markov games with continuous state space and finite action space. In the policy evaluation procedure of our algorithm, a novel kernel embedding method is employed to construct a finite-dimensional linear approximations to the state-action value function.

Here we omit the computation of HJ PDEs for concisity. The model is trained for 90, 000 iterations. The model is also trained for 90, 000 iterations. For the disentanglement methods, we largely enrich the original MNIST dataset by adding the transformed images of the whole sequence. The generalization ability ( i.e., validation accuracy) can be thus regarded as a reasonable surrogate for the disentanglement ability.

Our novel setup brings new understandings to both disentanglement and equivariance .

Novel view synthesis from sparse inputs is a vital yet challenging task in 3D computer vision. Previous methods explore 3D Gaussian Splatting with neural priors (e.g.

Although the population (i.e, infinite-particle) limit dynamics of SVGD is well characterized, its behavior in the finite-particle regime is far less understood.