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.
Bayesian inference engines improve density estimation accuracy and scalability.
Cost-aware SBI reduces expensive simulations in complex models.
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…
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide vari…
The article explores how organisms and machines learn and recognize the world using Bayesian inference and thermodynamics.
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing inferences from these models, but doing this successfully is severely hampered by the wide range of non--standard models they can express. As …
We introduce Dimple, a fully open-source API for probabilistic modeling. Dimple allows the user to specify probabilistic models in the form of graphical models, Bayesian networks, or factor graphs, and performs inference (by automatically deriving an inference engine from a variety of algorithms) on the model. Dimple a…
Unified framework for robust causal directionality in quantum systems under MNAR observation.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different…
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and epidemiology. In this paper we provide a fast approach for approximate Bayesian inference in SSMs using the tools of deep learning and variati…
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-stat…
New test assesses reliability of auto-generated features.
This paper tackles real-time Bayesian inverse problems using neural networks.
NP-HMC extends HMC for nonparametric models in probabilistic programming.
Unified framework improves robust causal inference, overcoming Gaussian barriers and optimization issues.
This paper introduces Probability Engineering to improve deep learning models.
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data eff…
Hybrid continuous-discrete models naturally represent many real-world applications in robotics, finance, and environmental engineering. Inference with large-scale models is challenging because relational structures deteriorate rapidly during inference with observations. The main contribution of this paper is an efficie…
Transformer models can approximate smooth functions with prompts, enhancing LLMs' dynamic capabilities.
Develops fully Bayesian LVGP for better uncertainty quantification.
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this Letter, we generalize VB by using quantum mechanics, and propose a new algorithm, which we call quantum annea…
SBI helps scientists match model outputs to data.
AIF improves physical AI agents' performance in dynamic environments.
Developing active inference agents for edge devices with limited resources.
This work optimizes statistical inference with neural networks for high-energy physics data.
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design c…
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to get more synthetic data that can be used to train downstream classifiers, it is not clear that they re…
A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of ne…
The paper develops a method to infer model parameters and shared dynamics from related physical systems using data.
New method ensures consistent inference across different tensor parallel sizes for large language models.
The paper analyzes distributed Bayesian inference and its Frequentist guarantees.
Paper shows how to infer hidden states in neural networks analytically.
Automates feature extraction from JSON data for machine learning.
Orpheus simplifies deep learning deployment on edge devices.
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data. We propose a new model that extends this inference scheme to multiple steps, arguing that it is easier to learn to improve a reconstruction in steps rather than to lea…
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of infer…
New method improves robustness and efficiency of inference from complex models.
Proposes a model for predicting events from event streams.
PriorGuide adapts diffusion models to new priors at test time.
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution …
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with…
Bayesian model transfers knowledge across different engineering fleets.
Inference-Time Scaling can be extended to domains prone to systematic failure using intrinsic statistics.
A new method for efficient inference in probabilistic programs with mixed support.
Agents learn state ambiguity from non-linear sensor data using Gaussian approximations.
Survey on statistical inference under memory constraints.
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 …