Search Results for author:
Found 11 papers, 1 papers with code
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
Quantitative and qualitative experiments show that our method outperforms the techniques based only on RGB images.
Ranked #3 on
3D Human Pose Estimation
on SLOPER4D
(using extra training data)
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
Our goal is to identify a near-optimal robust policy for the perturbed testing environment, which introduces additional technical difficulties as we need to simultaneously estimate the training environment uncertainty from samples and find the worst-case perturbation for testing.
Nearly Horizon-Free Offline Reinforcement Learning
To the best of our knowledge, these are the \emph{first} set of nearly horizon-free bounds for episodic time-homogeneous offline tabular MDP and linear MDP with anchor points.
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment.
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information
In this paper, we present Lazy-CFR, a CFR algorithm that adopts a lazy update strategy to avoid traversing the whole game tree in each round.
Fast Regularity-Constrained Plane Reconstruction
Man-made environments typically comprise planar structures that exhibit numerous geometric relationships, such as parallelism, coplanarity, and orthogonality.
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.
Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information
In this paper, we present a novel technique, lazy update, which can avoid traversing the whole game tree in CFR, as well as a novel analysis on the regret of CFR with lazy update.
Identify the Nash Equilibrium in Static Games with Random Payoffs
We study the problem on how to learn the pure Nash Equilibrium of a two-player zero-sum static game with random payoffs under unknown distributions via efficient payoff queries.
The YouTube-8M Kaggle Competition: Challenges and Methods
We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month's time.
Conditional Generative Moment-Matching Networks
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding.
