Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning. - Mariano Chouza; Stephen J. Roberts; Stefan Zohren (CoRR, 2018)

Entropic Spectral Learning in Large Scale Networks. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods. - Arnold Salas; Stefan Zohren; Stephen J. Roberts (CoRR, 2018)

Enhancing Time Series Momentum Strategies Using Deep Neural Networks. - Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2019)

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. - Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2019)

Deep Reinforcement Learning for Trading. - Zihao Zhang; Stefan Zohren; Stephen J. Roberts (CoRR, 2019)

Indian Buffet Neural Networks for Continual Learning. - Samuel Kessler; Vu Nguyen; Stefan Zohren; Stephen Roberts 001 (CoRR, 2019)

A Maximum Entropy approach to Massive Graph Spectra. - Diego Granziol; Robin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2019)

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (Entropy, 2019)

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. - Zihao Zhang; Stefan Zohren; Stephen J. Roberts (IEEE Trans. Signal Process., 2019)

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. - Bryan Lim; Stefan Zohren; Stephen Roberts 001 (IJCNN, 2020)

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. - Bryan Lim; Stefan Zohren; Stephen Roberts 001 (CoRR, 2020)

Time Series Forecasting With Deep Learning: A Survey. - Bryan Lim; Stefan Zohren (CoRR, 2020)

Deep Learning for Portfolio Optimisation. - Zihao Zhang; Stefan Zohren; Stephen Roberts 001 (CoRR, 2020)

Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects. - Peter Belcak; Jan-Peter Calliess; Stefan Zohren (CoRR, 2020)

Sentiment Diffusion in Financial News Networks and Associated Market Movements. - Xingchen Wan; Jie Yang; Slavi Marinov; Jan-Peter Calliess; Stefan Zohren; Xiaowen Dong 001 (CoRR, 2020)

Large Non-Stationary Noisy Covariance Matrices: A Cross-Validation Approach. - Vincent W. C. Tan; Stefan Zohren (CoRR, 2020)

Building Cross-Sectional Systematic Strategies By Learning to Rank. - Daniel Poh; Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2020)

Quantum Codes From Classical Graphical Models. - Joschka Roffe; Stefan Zohren; Dominic Horsman; Nicholas Chancellor (IEEE Trans. Inf. Theory, 2020)

Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects. - Peter Belcak; Jan-Peter Calliess; Stefan Zohren (MABS, 2021)

Hierarchical Indian buffet neural networks for Bayesian continual learning. - Samuel Kessler; Vu Nguyen; Stefan Zohren; Stephen J. Roberts (UAI, 2021)

Deep Learning for Market by Order Data. - Zihao Zhang; Bryan Lim; Stefan Zohren (CoRR, 2021)

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. - Daniel Poh; Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2021)

Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units. - Zihao Zhang; Stefan Zohren (CoRR, 2021)

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection. - Kieran Wood; Stephen J. Roberts; Stefan Zohren (CoRR, 2021)

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding. - Eghbal Rahimikia; Stefan Zohren; Ser-Huang Poon (CoRR, 2021)

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture. - Kieran Wood; Sven Giegerich; Stephen J. Roberts; Stefan Zohren (CoRR, 2021)

Same State, Different Task: Continual Reinforcement Learning without Interference. - Samuel Kessler; Jack Parker-Holder; Philip J. Ball; Stefan Zohren; Stephen J. Roberts (AAAI, 2022)

Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts. - Felix Drinkall; Stefan Zohren; Janet B. Pierrehumbert (NAACL-HLT, 2022)

Linnet: limit order books within switches. - Xinpeng Hong; Changgang Zheng; Stefan Zohren; Noa Zilberman (SIGCOMM Posters and Demos, 2022)

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. - Diego Granziol; Binxin Ru; Xiaowen Dong 001; Stefan Zohren; Michael A. Osborne; Stephen J. Roberts (Algorithms, 2022)

Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity. - Daniel Poh; Stephen J. Roberts; Stefan Zohren (CoRR, 2022)

DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions. - Fernando Moreno-Pino; Stefan Zohren (CoRR, 2022)

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training. - Diego Granziol; Stefan Zohren; Stephen Roberts 001 (J. Mach. Learn. Res., 2022)

LOBIN: In-Network Machine Learning for Limit Order Books. - Xinpeng Hong; Changgang Zheng; Stefan Zohren; Noa Zilberman (HPSR, 2023)

JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. - Sascha Yves Frey; Kang Li; Peer Nagy; Silvia Sapora; Christopher Lu 001; Stefan Zohren; Jakob N. Foerster; Anisoara Calinescu (ICAIF, 2023)

Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network. - Peer Nagy; Sascha Frey; Silvia Sapora; Kang Li; Anisoara Calinescu; Stefan Zohren; Jakob N. Foerster (ICAIF, 2023)

Dynamic Time Warping for Lead-Lag Relationship Detection in Lagged Multi-Factor Models. - Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren (ICAIF, 2023)

Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets. - Peer Nagy; Jan-Peter Calliess; Stefan Zohren (CoRR, 2023)

Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies. - Wee Ling Tan; Stephen J. Roberts; Stefan Zohren (CoRR, 2023)

Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models. - Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren (CoRR, 2023)

Network Momentum across Asset Classes. - Xingyue Pu; Stephen J. Roberts; Xiaowen Dong 001; Stefan Zohren (CoRR, 2023)

Learning to Learn Financial Networks for Optimising Momentum Strategies. - Xingyue Pu; Stefan Zohren; Stephen J. Roberts; Xiaowen Dong 001 (CoRR, 2023)

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies. - Kieran Wood; Samuel Kessler; Stephen J. Roberts; Stefan Zohren (CoRR, 2023)

Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets. - Peer Nagy; Jan-Peter Calliess; Stefan Zohren (Frontiers Artif. Intell., 2023)

On Sequential Bayesian Inference for Continual Learning. - Samuel Kessler; Adam D. Cobb; Tim G. J. Rudner; Stefan Zohren; Stephen J. Roberts (Entropy, 2023)

Accelerating Machine Learning for Trading Using Programmable Switches. - Xinpeng Hong; Changgang Zheng; Stefan Zohren; Noa Zilberman (ECAI, 2024)

Extracting Alpha from Financial Analyst Networks. - Dragos Gorduza; Yaxuan Kong; Xiaowen Dong 001; Stefan Zohren (ICAIF, 2024)

Deep Learning for Options Trading: An End-To-End Approach. - Wee Ling Tan; Stephen Roberts 001; Stefan Zohren (ICAIF, 2024)

The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. - Sanjay Purushotham; Dongjin Song; Qingsong Wen; Jun Huan; Cong Shen 001; Stefan Zohren; Yuriy Nevmyvaka (KDD, 2024)

Time Machine GPT. - Felix Drinkall; Eghbal Rahimikia; Janet B. Pierrehumbert; Stefan Zohren (NAACL-HLT (Findings), 2024)

A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges. - Yuqi Nie; Yaxuan Kong; Xiaowen Dong 001; John M. Mulvey; H. Vincent Poor; Qingsong Wen; Stefan Zohren (CoRR, 2024)

Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management. - Yoontae Hwang; Stefan Zohren; Yongjae Lee (CoRR, 2024)

Traditional Methods Outperform Generative LLMs at Forecasting Credit Ratings. - Felix Drinkall; Janet B. Pierrehumbert; Stefan Zohren (CoRR, 2024)

Unlocking the Power of LSTM for Long Term Time Series Forecasting. - Yaxuan Kong; Zepu Wang; Yuqi Nie; Tian Zhou 004; Stefan Zohren; Yuxuan Liang; Peng Sun; Qingsong Wen (AAAI, 2025)

Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups. - Felix Drinkall; Stefan Zohren; Michael McMahon; Janet B. Pierrehumbert (ACL (Findings), 2025)

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement. - Yaxuan Kong; Yiyuan Yang; Yoontae Hwang; Wenjie Du; Stefan Zohren; Zhangyang Wang; Ming Jin 005; Qingsong Wen (ACL (1), 2025)

Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs. - Felix Drinkall; Janet B. Pierrehumbert; Stefan Zohren (COLING Workshops, 2025)

Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization. - Yoontae Hwang; Yaxuan Kong; Stefan Zohren; Yongjae Lee (CoRR, 2025)

Position: Empowering Time Series Reasoning with Multimodal LLMs. - Yaxuan Kong; Yiyuan Yang; Shiyu Wang 001; Chenghao Liu; Yuxuan Liang; Ming Jin 005; Stefan Zohren; Dan Pei; Yan Liu 069; Qingsong Wen (CoRR, 2025)

When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks. - Felix Drinkall; Janet B. Pierrehumbert; Stefan Zohren (CoRR, 2025)

LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data. - Peer Nagy; Sascha Frey; Kang Li; Bidipta Sarkar; Svitlana Vyetrenko; Stefan Zohren; Ani Calinescu; Jakob N. Foerster (CoRR, 2025)

Understanding stock market instability via graph auto-encoders. - Dragos Gorduza; Stefan Zohren; Xiaowen Dong 001 (EPJ Data Sci., 2025)