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1 code implementation • ECCV 2020 • Yuanhao Wang, Ramzi Idoughi, Wolfgang Heidrich

Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows.

no code implementations • 25 Jun 2023 • Yuanhao Wang, Qinghua Liu, Chi Jin

This paper theoretically proves that, for a wide range of preference models, we can solve preference-based RL directly using existing algorithms and techniques for reward-based RL, with small or no extra costs.

no code implementations • 13 Feb 2023 • Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing learning algorithms scale exponentially with the number of agents.

no code implementations • 20 Oct 2022 • Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin

This paper develops the first line of efficient algorithms for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE) whose sample complexities are polynomial in all problem parameters including the number of players.

no code implementations • 14 Mar 2022 • Qinghua Liu, Yuanhao Wang, Chi Jin

When the policies of the opponents are not revealed, we prove a statistical hardness result even in the most favorable scenario when both above conditions are true.

no code implementations • 28 Feb 2022 • Rui Li, Darius Rückert, Yuanhao Wang, Ramzi Idoughi, Wolfgang Heidrich

Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost.

1 code implementation • 4 Feb 2022 • Darius Rückert, Yuanhao Wang, Rui Li, Ramzi Idoughi, Wolfgang Heidrich

Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods.

no code implementations • 27 Oct 2021 • Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu

We design a new class of fully decentralized algorithms -- V-learning, which provably learns Nash equilibria (in the two-player zero-sum setting), correlated equilibria and coarse correlated equilibria (in the multiplayer general-sum setting) in a number of samples that only scales with $\max_{i\in[m]} A_i$, where $A_i$ is the number of actions for the $i^{\rm th}$ player.

no code implementations • NeurIPS 2021 • Yuanhao Wang, Ruosong Wang, Sham M. Kakade

The recent and remarkable result of Weisz et al. (2020) resolves this question in the negative, providing an exponential (in $d$) sample size lower bound, which holds even if the agent has access to a generative model of the environment.

no code implementations • NeurIPS 2021 • Yuanhao Wang, Ruosong Wang, Sham M. Kakade

This work focuses on this question in the standard online reinforcement learning setting, where our main result resolves this question in the negative: our hardness result shows that an exponential sample complexity lower bound still holds even if a constant suboptimality gap is assumed in addition to having a linearly realizable optimal $Q$-function.

no code implementations • 18 Feb 2021 • Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger Grosse

Smooth minimax games often proceed by simultaneous or alternating gradient updates.

no code implementations • 28 Oct 2020 • Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra

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.

no code implementations • 21 Aug 2020 • Yuanhao Wang, Kefan Dong

We consider the adversarial Markov Decision Process (MDP) problem, where the rewards for the MDP can be adversarially chosen, and the transition function can be either known or unknown.

no code implementations • 17 Aug 2020 • Guodong Zhang, Yuanhao Wang

Smooth game optimization has recently attracted great interest in machine learning as it generalizes the single-objective optimization paradigm.

no code implementations • NeurIPS 2020 • Yuanhao Wang, Jian Li

This paper studies minimax optimization problems $\min_x \max_y f(x, y)$, where $f(x, y)$ is $m_x$-strongly convex with respect to $x$, $m_y$-strongly concave with respect to $y$ and $(L_x, L_{xy}, L_y)$-smooth.

no code implementations • ICLR 2020 • Yuanhao Wang, Guodong Zhang, Jimmy Ba

Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games.

1 code implementation • 7 May 2019 • Guocheng Qian, Yuanhao Wang, Jinjin Gu, Chao Dong, Wolfgang Heidrich, Bernard Ghanem, Jimmy S. Ren

In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.

no code implementations • ICLR 2020 • Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, Li-Wei Wang

We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server.

no code implementations • ICLR 2020 • Kefan Dong, Yuanhao Wang, Xiaoyu Chen, Li-Wei Wang

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.

1 code implementation • 19 May 2018 • Zheng-Heng Li, Merlin Kole, Jian-Chao Sun, Li-Ming Song, Nicolas Produit, Bo-Bing Wu, Tianwei Bao, Tancredi Bernasconi, Franck Cadoux, Yongwei Dong, Minzi Feng, Neal Gauvin, Wojtek Hajdas, Hancheng Li, Lu Li, Xin Liu, Radoslaw Marcinkowski, Martin Pohl, Dominik K. Rybka, Haoli Shi, Jacek Szabelski, Teresa Tymieniecka, Ruijie Wang, Yuanhao Wang, Xing Wen, Xin Wu, Shao-Lin Xiong, Anna Zwolinska, Li Zhang, Lai-Yu Zhang, Shuang-Nan Zhang, Yong-Jie Zhang, Yi Zhao

POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.

Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors

no code implementations • 11 Jan 2018 • Yuanhao Wang, Ying Li, Zhang-qi Yin, Bei Zeng

Entanglement is an important evidence that a quantum device can potentially solve problems intractable for classical computers.

Quantum Physics

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