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Found 8 papers, 0 papers with code
PhysBench: A Benchmark Framework for Remote Physiological Sensing with New Dataset and Baseline
In recent years, due to the widespread use of internet videos, physiological remote sensing has gained more and more attention in the fields of affective computing and telemedicine.
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system.
Byzantine-Robust Federated Linear Bandits
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model.
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable.
Provably Efficient Algorithms for Multi-Objective Competitive RL
To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal.
Online Learning in Unknown Markov Games
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable.
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions.
Ranked #3 on
Skeleton Based Action Recognition
on UT-Kinect
