activity
20242026
most citedImproving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling

1 citations · 1 across the 3 of their papers we have counts for

collaborators

10 papers

cs.LG2026

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach

Lawrence Clegg, John Cartlidge

Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis. Yet, there are few known attempts to incorporate it within forecas…

cs.LG2026

VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting

Weibin Feng, Ran Tao, John Cartlidge +1

Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal pattern…

cs.LG20261 cited

Improving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling

Zinuo You, John Cartlidge, Karen Elliott +2

Existing black-box portfolio management systems are prevalent in the financial industry due to commercial and safety constraints, though their performance can fluctuate dramaticall…

q-fin.RM2026

Systemic Risk in DeFi: A Network-Based Fragility Analysis of TVL Dynamics

Shiyu Zhang, Zining Wang, Jin Zheng +1

Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing dece…

q-fin.CP2025

BondBERT: What we learn when assigning sentiment in the bond market

Toby Barter, Zheng Gao, Eva Christodoulaki +2

Bond markets respond differently to macroeconomic news compared to equity markets, yet most sentiment models are trained primarily on general financial or equity news data. However…

cs.LG2025

How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation

Zinuo You, Jin Zheng, John Cartlidge

Existing graph neural networks typically rely on heuristic choices for hidden dimensions and propagation depths, which often lead to severe information loss during propagation, kno…