A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep sum-product networks learn faster than shallow models.
problem The speed of parameter optimization in sum-product networks.
method Theoretical analysis and empirical experiments on overparameterized sum-product networks.
result Gradient-based optimization in deep sum-product networks is equivalent to gradient ascent with adaptive and time-varying learning rates and additional momentum terms.
We propose an algorithm for the adaptation of the learning rate for stochastic gradient descent (SGD) that avoids the need for validation set use. The idea for the adaptiveness comes from the technique of extrapolation: to get an estimate for the error against the gradient flow which underlies SGD, we compare the resul…
We describe a novel algorithm for noisy global optimisation and continuum-armed bandits, with good convergence properties over any continuous reward function having finitely many polynomial maxima. Over such functions, our algorithm achieves square-root regret in bandits, and inverse-square-root error in optimisation, …
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent…
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information. In this work we propose a direct least-squares approach conceptually similar to…
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' …
Unified Bayesian Optimisation for mixed variables improves performance.
problem Efficient optimisation of problems with both categorical and continuous variables.
method Derive value proposals from the Expected Improvement criterion to optimise both categorical and continuous variables under a single acquisition metric.
result Unified approach significantly outperforms existing methods across mixed-variable tasks.
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal o…
Higher conservative training increases reward-hacking in reasoning models.
problem Reward hacking during online adaptation in reasoning models.
method Conservative offline training with varying levels of conservatism (β) was applied to a Qwen3-14B policy, and online adaptation was measured against a reward ensemble.
result Higher conservatism (β) increases reward-hacking damage, measured by the Goodhart gap and AUGC.
Deep RL algorithms struggle with noisy rewards in portfolio optimisation.
problem Evaluating deep reinforcement learning for portfolio optimisation with market impact.
method Simulated data with geometric Brownian motion and market impact model; Kelly criterion as upper bound; PPO and A2C with GAE; clipping; hidden Markov model for regime changes.
result PPO and A2C with GAE perform better with noisy rewards; PPO with HMM learns different policies for regime changes.