42 citations · 97 across the 17 of their papers we have counts for
20 papers
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Lei Jiang, Ye Wei, Xinyu Xi +7
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs c…
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
Kaicheng Zhang, Wen Ge, Lei Jiang +5
Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial rele…
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Yifan Bao, Xinyu Xi, Xinyu Liu +8
Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation p…
Pricing and hedging of decentralised lending contracts
Lukasz Szpruch, Marc Sabaté Vidales, Tanut Treetanthiploet +1
We study the loan contracts offered by decentralised loan protocols (DLPs) through the lens of financial derivatives. DLPs, which effectively are clearinghouses, facilitate transac…
-Policy Gradient for Online Pricing
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an -policy gradient algorithm for the online pricing learning task. The…
Mirror Descent for Stochastic Control Problems with Measure-valued Controls
Bekzhan Kerimkulov, David Šiška, Łukasz Szpruch +1
This paper studies the convergence of the mirror descent algorithm for finite horizon stochastic control problems with measure-valued control processes. The control objective invol…
A Fisher-Rao gradient flow for entropy-regularised Markov decision processes in Polish spaces
Bekzhan Kerimkulov, James-Michael Leahy, David Siska +2
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. The flow…
Insurance pricing on price comparison websites via reinforcement learning
Tanut Treetanthiploet, Yufei Zhang, Lukasz Szpruch +4
The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to…
Inefficiency of CFMs: hedging perspective and agent-based simulations
Samuel Cohen, Marc Sabaté Vidales, David Šiška +1
We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk. We first analyse this problem thro…
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning
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. Herein agent interacts…
Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime
Bekzhan Kerimkulov, James-Michael Leahy, David Šiška +1
We study the global convergence of policy gradient for infinite-horizon, continuous state and action space, and entropy-regularized Markov decision processes (MDPs). We consider a…
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting. We then apply it to study finite-time horizon stochastic control prob…
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
Solving path dependent PDEs with LSTM networks and path signatures
Marc Sabate-Vidales, David Šiška, Lukasz Szpruch
Using a combination of recurrent neural networks and signature methods from the rough paths theory we design efficient algorithms for solving parametric families of path dependent…
Robust pricing and hedging via neural SDEs
Patryk Gierjatowicz, Marc Sabate-Vidales, David Šiška +2
Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of usi…
Sig-SDEs model for quantitative finance
Imanol Perez Arribas, Cristopher Salvi, Lukasz Szpruch
Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-d…
Gradient Flows for Regularized Stochastic Control Problems
David Šiška, Łukasz Szpruch
This paper studies stochastic control problems with the action space taken to be probability measures, with the objective penalised by the relative entropy. We identify suitable me…
Conditional Sig-Wasserstein GANs for Time Series Generation
Shujian Liao, Hao Ni, Lukasz Szpruch +3
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle t…
Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks
Kaitong Hu, Zhenjie Ren, David Siska +1
Our work is motivated by a desire to study the theoretical underpinning for the convergence of stochastic gradient type algorithms widely used for non-convex learning tasks such as…
Unbiased deep solvers for linear parametric PDEs
Marc Sabate Vidales, David Siska, Lukasz Szpruch
We develop several deep learning algorithms for approximating families of parametric PDE solutions. The proposed algorithms approximate solutions together with their gradients, whi…