6 papers
Stacking the Deck: Tunable Trainability in Stacked LCUs
Nikhil Khatri, Stefan Zohren, Gabriel Matos
Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests that trainability and quantum a…
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
Kieran Wood, Stefan Zohren, Stephen J. Roberts
We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes from the underlying signal and…
Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts +1
The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimi…
DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management
Kieran Wood, Stephen J. Roberts, Stefan Zohren
We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three…
Trainability of Parametrised Linear Combinations of Unitaries
Nikhil Khatri, Stefan Zohren, Gabriel Matos
A principal concern in the optimisation of parametrised quantum circuits is the presence of barren plateaus, which present fundamental challenges to the scalability of applications…
On Sequential Bayesian Inference for Continual Learning
Samuel Kessler, Adam Cobb, Tim G. J. Rudner +2
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revis…