Search Results for author:
Found 23 papers, 14 papers with code
Rao-Blackwellising Bayesian Causal Inference
Specifically, we decompose the problem of inferring the causal structure into (i) inferring a topological order over variables and (ii) inferring the parent sets for each variable.
Probabilistic Integral Circuits
In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units.
How to Turn Your Knowledge Graph Embeddings into Generative Models
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models.
Ranked #3 on
Link Property Prediction
on ogbl-biokg
Bayesian Structure Scores for Probabilistic Circuits
Probabilistic circuits (PCs) are a prominent representation of probability distributions with tractable inference.
Continuous Mixtures of Tractable Probabilistic Models
Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models.
Active Bayesian Causal Inference
In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.
Towards Robust Classification with Deep Generative Forests
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets.
Joints in Random Forests
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines.
Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches.
Sum-Product Network Decompilation
Due to this dichotomy, tools to convert between BNs and SPNs are desirable.
Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs.
Bayesian Learning of Sum-Product Networks
While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them are somewhat ad-hoc and based on intuition rather than a clear learning principle.
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.
Optimisation of Overparametrized Sum-Product Networks
It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models.
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
Efficient and Robust Machine Learning for Real-World Systems
In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference.
Automatic Bayesian Density Analysis
Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.
Probabilistic Deep Learning using Random Sum-Product Networks
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods.
Safe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant.
On the Latent Variable Interpretation in Sum-Product Networks
We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks.
