no code implementations • 5 Apr 2024 • Yufei Zhang, Jeffrey O. Kephart, Zijun Cui, Qiang Ji
PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner.
no code implementations • 18 Dec 2023 • Chenlu Zhan, Yufei Zhang, Yu Lin, Gaoang Wang, Hongwei Wang
Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application.
no code implementations • 4 Oct 2023 • Bekzhan Kerimkulov, James-Michael Leahy, David Siska, Lukasz Szpruch, Yufei Zhang
We study the global convergence of a Fisher-Rao policy gradient flow for infinite-horizon entropy-regularised Markov decision processes with Polish state and action space.
no code implementations • 6 Sep 2023 • Eyal Neuman, Wolfgang Stockinger, Yufei Zhang
We show that a trader who tries to minimise her execution costs by using a greedy strategy purely based on the estimated propagator will encounter suboptimality due to so-called spurious correlation between the trading strategy and the estimator and due to intrinsic uncertainty resulting from a biased cost functional.
no code implementations • 14 Aug 2023 • Tanut Treetanthiploet, Yufei Zhang, Lukasz Szpruch, Isaac Bowers-Barnard, Henrietta Ridley, James Hickey, Chris Pearce
The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies.
no code implementations • ICCV 2023 • Yufei Zhang, Hanjing Wang, Jeffrey O. Kephart, Qiang Ji
While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for training.
no code implementations • 25 Jun 2023 • Melker Hoglund, Emilio Ferrucci, Camilo Hernandez, Aitor Muguruza Gonzalez, Cristopher Salvi, Leandro Sanchez-Betancourt, Yufei Zhang
We propose a novel framework for solving continuous-time non-Markovian stochastic control problems by means of neural rough differential equations (Neural RDEs) introduced in Morrill et al. (2021).
no code implementations • 12 Jan 2023 • Eyal Neuman, Yufei Zhang
For the exploration phase we propose a novel approach for non-parametric estimation of the price impact kernel by observing only the visible price process and derive sharp bounds on the convergence rate, which are characterised by the singularity of the propagator.
no code implementations • 1 Nov 2022 • Michael Giegrich, Christoph Reisinger, Yufei Zhang
We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems.
no code implementations • 13 Oct 2022 • Tan Yu, Jun Zhi, Yufei Zhang, Jian Li, Hongliang Fei, Ping Li
In this paper, we formulate the APP-installation user embedding learning into a bipartite graph embedding problem.
no code implementations • 8 Aug 2022 • Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms.
no code implementations • 1 Jun 2022 • Yufei Zhang, Arno Schlüter, Christoph Waibel
Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes.
no code implementations • 22 Mar 2022 • Christoph Reisinger, Wolfgang Stockinger, Yufei Zhang
Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive.
no code implementations • 19 Dec 2021 • Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting.
Model-based Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 13 May 2021 • Zidu Wang, Xuexin Liu, Long Huang, Yunqing Chen, Yufei Zhang, Zhikang Lin, Rui Wang
In this paper, we propose a novel theory to find redundant information in three-dimensional tensors, namely Quantified Similarity between Feature Maps (QSFM), and utilize this theory to guide the filter pruning procedure.
no code implementations • 19 Apr 2021 • Xin Guo, Anran Hu, Yufei Zhang
We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting.
1 code implementation • NeurIPS 2020 • Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song
Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
no code implementations • 5 Oct 2020 • Runze Li, Yufei Zhang, Haixin Chen
The policy is then trained in environments based on surrogate models, of which the mean drag reduction of 200 airfoils can be effectively improved by reinforcement learning.
Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability
no code implementations • 27 Jun 2020 • Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang
We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller.
1 code implementation • 24 Jun 2020 • Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
no code implementations • 9 Jan 2020 • Christoph Reisinger, Yufei Zhang
This paper proposes a relaxed control regularization with general exploration rewards to design robust feedback controls for multi-dimensional continuous-time stochastic exit time problems.
no code implementations • 5 Jun 2019 • Kazufumi Ito, Christoph Reisinger, Yufei Zhang
In this work, we propose a class of numerical schemes for solving semilinear Hamilton-Jacobi-Bellman-Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift.
no code implementations • 15 Mar 2019 • Christoph Reisinger, Yufei Zhang
In this paper, we establish that for a wide class of controlled stochastic differential equations (SDEs) with stiff coefficients, the value functions of corresponding zero-sum games can be represented by a deep artificial neural network (DNN), whose complexity grows at most polynomially in both the dimension of the state equation and the reciprocal of the required accuracy.