Search Results for author:
Found 12 papers, 9 papers with code
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Up to a logarithmic dependence on the size of the state space, Lin-Confident-FTRL learns $\epsilon$-CCE with a provable optimal accuracy bound $O(\epsilon^{-2})$ and gets rids of the linear dependency on the action space, while scaling polynomially with relevant problem parameters (such as the number of agents and time horizon).
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes.
High-Quality Facial Geometry and Appearance Capture at Home
Facial geometry and appearance capture have demonstrated tremendous success in 3D scanning real humans in studios.
PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost.
Learning a 3D Morphable Face Reflectance Model from Low-cost Data
This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data.
Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles
We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form.
On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits
To address such a challenge as the online convex optimization with privacy protection, we propose a private variant of online Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data.
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images
This paper deals with the challenging task of synthesizing novel views for in-the-wild photographs.
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
In this paper, we propose a general framework to solve the above two challenges simultaneously.
Generalized Linear Bandits with Local Differential Privacy
In this paper, we design LDP algorithms for stochastic generalized linear bandits to achieve the same regret bound as in non-privacy settings.
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search
We further propose a Disentanglement-Transformation (DT) metric to quantify the attribute transformation and disentanglement efficacy and find the optimal control factor between attribute-level and instance-specific directions based on it.
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs).
