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.
PPL improves on Takacs-Fiksel estimation for Gibbs models.
DMVI uses diffusion models for efficient probabilistic inference in PPLs.
NP-iMCMC algorithm for nonparametric models in universal PPLs.
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the …
Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages probabilistic models for this task. Most BO today uses Gaussian processes (GPs), or a few other surr…
PClean automates Bayesian data cleaning for specific datasets.
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference methods used in PPL can be computationally costly due to significant time burden and/or storage requirements; or they can lack theoretical g…
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could tr…
Improves sampling from complex hierarchical models using HMC and automatic marginalization.
Improves Bayesian optimisation for engineering design problems with many variables.
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwellization via delayed sampling. Birth-death models of evolution are an important family of phylogenetic…
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs). However, the fact that HMC uses derivative information causes complications when the target distribution is non-differentiable with respect…
We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density functio…
Type system captures CI relationships for probabilistic models.
LIC compiles probabilistic models to generate efficient MCMC proposals.
NP-HMC extends HMC for nonparametric models in probabilistic programming.
Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates …
A new framework for PPLS combines noise estimation, optimization, and calibration.
We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also fi…
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understandi…
New algorithm learns policies without uniform overlap assumption.
dynestyx: A library for probabilistic programming of dynamical systems
The front-end factor analysis (FEFA), an extension of principal component analysis (PPCA) tailored to be used with Gaussian mixture models (GMMs), is currently the prevalent approach to extract compact utterance-level features (i-vectors) for automatic speaker verification (ASV) systems. Little research has been conduc…
ProbDR framework interprets DR algorithms as probabilistic inference.
Unified continuous diffusion model outperforms discrete alternatives in scalability and quality.
This research explores discrete diffusion models for natural language generation.
This paper studies activation sparsity in large language models, finding key trends and implications.
Improved autoencoder boosts sequence learning with less memory.
ADAHESSIAN optimizes machine learning models with adaptive second-order methods.