Search Results for author:
Found 11 papers, 5 papers with code
Diffusion for Natural Image Matting
However, the presence of high computational overhead and the inconsistency of noise sampling between the training and inference processes pose significant obstacles to achieving this goal.
Ranked #1 on
Image Matting
on Distinctions-646
Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games
The algorithm is scalable since each agent uses only local information and does not need access to the global state.
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems
This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting.
Online Optimization with Feedback Delay and Nonlinear Switching Cost
We study a variant of online optimization in which the learner receives $k$-round $\textit{delayed feedback}$ about hitting cost and there is a multi-step nonlinear switching cost, i. e., costs depend on multiple previous actions in a nonlinear manner.
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state and action spaces are exponentially large in the number of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
We study multi-agent reinforcement learning (MARL) in a stochastic network of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Online Optimization with Memory and Competitive Control
This paper presents competitive algorithms for a novel class of online optimization problems with memory.
Online Optimization with Predictions and Non-convex Losses
In this work, we give two general sufficient conditions that specify a relationship between the hitting and movement costs which guarantees that a new algorithm, Synchronized Fixed Horizon Control (SFHC), provides a $1+O(1/w)$ competitive ratio, where $w$ is the number of predictions available to the learner.
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strongly convex and the movement costs are the squared $\ell_2$ norm.
