Search Results for author:
Found 21 papers, 1 papers with code
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Our analysis is grounded in a novel parametric form of the neural network and an innovative connection between score matching and regression analysis, facilitating the application of advanced statistical and optimization techniques.
Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
This paper proposes and analyzes two new policy learning methods: regularized policy gradient (RPG) and iterative policy optimization (IPO), for a class of discounted linear-quadratic control (LQC) problems over an infinite time horizon with entropy regularization.
Risk-sensitive Markov Decision Process and Learning under General Utility Functions
In this paper, we consider a scenario where the decision-maker seeks to optimize a general utility function of the cumulative reward in the framework of a Markov decision process (MDP).
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications.
Asymptotic Analysis of Deep Residual Networks
We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases.
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
Then, we rigorously analyze their near-optimal regret upper bounds to show that, by leveraging the linear structure, our algorithms can dramatically reduce the regret when compared to existing methods.
Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks
We prove linear convergence of gradient descent to a global optimum for the training of deep residual networks with constant layer width and smooth activation function.
Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios.
Recent Advances in Reinforcement Learning in Finance
In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
This paper proposes a framework of localized training and decentralized execution to study MARL with network of states.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium.
Scaling Properties of Deep Residual Networks
Residual networks (ResNets) have displayed impressive results in pattern recognition and, recently, have garnered considerable theoretical interest due to a perceived link with neural ordinary differential equations (neural ODEs).
Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon
In particular, we consider the convergence of policy gradient methods in the setting of known and unknown parameters.
Model-free Analysis of Dynamic Trading Strategies
We introduce a model-free approach based on excursions of trading signals for analyzing the risk and return for a broad class of dynamic trading strategies, including pairs trading and other statistical arbitrage strategies.
Entropy Regularization for Mean Field Games with Learning
In addition, this study leads to a policy-gradient algorithm for exploration in MFG.
A General Framework for Learning Mean-Field Games
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population.
Delay-Adaptive Learning in Generalized Linear Contextual Bandits
In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed.
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality.
Learning in Generalized Linear Contextual Bandits with Stochastic Delays
In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed.
Transaction Cost Analytics for Corporate Bonds
This part is on the time scale of each transaction of liquid corporate bonds, and is by applying a transient impact model to estimate the price impact kernel using a non-parametric method.
A class of stochastic games and moving free boundary problems
In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case.
