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.
DADVI improves ADVI by using deterministic approximation for faster, more accurate posterior estimation.
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, wh…
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate. We propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI). …
ADVI speeds up Bayesian inference for bridge regression models.
New method learns complex, multimodal distributions in ADVI.
This paper introduces a spline-based method for nonparametric ADVI that handles complex posterior distributions.
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, TrustVI proposes and assesses a step based on minibatches of draws from the variational distribution. The algorithm provably converges to a stat…
Pathfinder uses quasi-Newton optimization for variational inference.
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming para…
New method simplifies Bayesian analysis for categorical data.
CAVI speeds up Bayesian MIDAS regression by 107x-1,772x with similar accuracy.