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.
Framework simulates market microstructure with stable Hawkes processes.
Adaptive stepsizing improves sampling in Bayesian neural networks.
When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequ…
A new method reformulates Optimal Transport Conditional Flow Matching using proximal operators.
This research accelerates sampling methods using Nesterov's Acceleration.
New method improves uncertainty quantification in latent variable models.
New insights into SGD and SGD-M in high dimensions.
Adaptive-stepsize MCMC sampling inspired by Adam optimizer.
Actor-critic algorithms converge to an ODE as data samples change dynamically.
Gradient descent-based optimization methods underpin the parameter training of neural networks, and hence comprise a significant component in the impressive test results found in a number of applications. Introducing stochasticity is key to their success in practical problems, and there is some understanding of the rol…