Robert Peharz
22 papers · 2013–2025 · 9 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+9 more ↓ Show less ↑
π Conference Polyglot (9) πΊοΈ Taxonomy Completionist (13) π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (12)
π
Academic Marathon
(12)
πΊοΈ
Taxonomy Completionist
(13)
π
Triple Crown
π§¬
Topic Evolution
π
Grand Slam
ποΈ
Keyword Collector
(77)
β‘
Prolific Year
(6)
π
Conference Pioneer
π
Century Club
(22)
Conferences
AISTATS (5)
ICML (5)
NIPS (4)
AAAI (2)
PGM (2)
ICLR (1)
INTERSPEECH (1)
JMLR (1)
UAI (1)
Top co-authors
Keywords
sum-product network
(7)
probabilistic circuit
(5)
bayesian inference
(5)
generative model
(5)
graphical model
(4)
probabilistic modeling
(3)
density estimation
(3)
variational autoencoder
(3)
tractable inference
(3)
anomaly detection
(2)
variational inference
(2)
structure learning
(2)
random forest
(2)
probabilistic graphical model
(2)
gaussian process
(2)
bayesian network
(2)
support vector machine
(2)
network pruning
(1)
uncertainty quantification
(1)
causal discovery
(1)
Papers
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
AISTATS 2025
Probabilistic Integral Circuits
AISTATS 2024
Exact Soft Analytical Side-Channel Attacks using Tractable Circuits
ICML 2024
Resource-Efficient Neural Networks for Embedded Systems
JMLR 2024
How to Turn Your Knowledge Graph Embeddings into Generative Models
NIPS 2023
Bayesian Structure Scores for Probabilistic Circuits
AISTATS 2023
Continuous Mixtures of Tractable Probabilistic Models
AAAI 2023
Active Bayesian Causal Inference
NIPS 2022
Sum-Product Network Decompilation
PGM 2020
Joints in Random Forests
NIPS 2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
ICML 2020
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
PGM 2020
Deep Structured Mixtures of Gaussian Processes
AISTATS 2020
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
UAI 2019
Automatic Bayesian Density Analysis
AAAI 2019
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
ICLR 2019
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
ICML 2019
Hierarchical Decompositional Mixtures of Variational Autoencoders
ICML 2019
Bayesian Learning of Sum-Product Networks
NIPS 2019
Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development
INTERSPEECH 2016
On Theoretical Properties of Sum-Product Networks
AISTATS 2015
The Most Generative Maximum Margin Bayesian Networks
ICML 2013