103 citations · 272 across the 64 of their papers we have counts for
52 papers · 1 filter
MERIT: Scaling KG Foundation Models with Relation Transformers
Cosimo Gregucci, Daniel Hernandez, Antonio Vergari +1
Knowledge graph (KG) foundation models (KGFMs) predict links on unseen graphs zero-shot. KGFMs have been pioneered by ULTRA which builds a relation graph from co-occurrences that m…
When Are Concept Bottleneck Model Explanations Faithful and Compact?
Stefano Teso, Emanuele Marconato, Steve Azzolin +1
Concept bottleneck models (CBMs) are neural classifiers that allow to explain their decisions via high-level concepts, potentially enabling understanding, steering and debugging. H…
Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval
Nickil Maveli, Antonio Vergari, Shay B. Cohen
Large language models (LLMs) have demonstrated strong performance in code generation, where success depends on both recalling relevant algorithmic knowledge and reasoning about how…
Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations
Christian Jimenez-Beltran, Aretha L. Teckentrup, Antonio Vergari +1
Inverse problems for differential equations arise throughout science and engineering, where one seeks to infer unknown model parameters from noisy or incomplete observations. Tradi…
Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
Cosimo Gregucci, Obaidah Theeb, Daniel Hernandez +2
Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining. However, understanding when and…
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
Even More Guarantees for Variational Inference in the Presence of Symmetries
Lena Zellinger, Antonio Vergari
When approximating an intractable density via variational inference (VI) the variational family is typically chosen as a simple parametric family that very likely does not contain…
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Weixin Chen, Antonio Vergari, Han Zhao
Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper…
A Sobering Look at Tabular Data Generation via Probabilistic Circuits
Davide Scassola, Dylan Ponsford, Adrián Javaloy +4
Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion-based models are the curre…
The Theory and Practice of MAP Inference over Non-Convex Constraints
Leander Kurscheidt, Gabriele Masina, Roberto Sebastiani +1
In many safety-critical settings, probabilistic ML systems have to make predictions subject to algebraic constraints, e.g., predicting the most likely trajectory that does not cros…
An Embarrassingly Simple Way to Optimize Orthogonal Matrices at Scale
Adrián Javaloy, Antonio Vergari
Orthogonality constraints are ubiquitous in robust and probabilistic machine learning. Unfortunately, current optimizers are computationally expensive and do not scale to problems…
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility
Nickil Maveli, Antonio Vergari, Shay B. Cohen
LLMs demonstrate strong performance on code benchmarks, yet consistent reasoning across forward and backward execution remains elusive. We present RoundTripCodeEval (RTCE), a bench…
How to Square Tensor Networks and Circuits Without Squaring Them
Lorenzo Loconte, Adrián Javaloy, Antonio Vergari
Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimators, yet supporting closed-form margin…
Can LLMs subtract numbers?
Mayank Jobanputra, Nils Philipp Walter, Maitrey Mehta +7
We present a systematic study of subtraction in large language models (LLMs). While prior benchmarks emphasize addition and multiplication, subtraction has received comparatively l…
Fast and Expressive Multi-Byte Prediction with Probabilistic Circuits
Andreas Grivas, Lorenzo Loconte, Emile van Krieken +6
Multi-token prediction (MTP) is a prominent strategy to significantly speed up generation in large language models (LLMs), especially in byte-level LLMs, which are tokeniser-free b…
Tractable Representation Learning with Probabilistic Circuits
Steven Braun, Sahil Sidheekh, Antonio Vergari +3
Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference task…
Neurosymbolic Reasoning Shortcuts under the Independence Assumption
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
The ubiquitous independence assumption among symbolic concepts in neurosymbolic (NeSy) predictors is a convenient simplification: NeSy predictors use it to speed up probabilistic r…
Neurosymbolic Diffusion Models
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predictors assume conditional indepen…
Scalable Expectation Estimation with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Víctor Elvira +1
Many Monte Carlo (MC) and importance sampling (IS) methods use mixture models (MMs) for their simplicity and ability to capture multimodal distributions. Recently, subtractive mixt…
A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction
Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani +2
In safety-critical applications, guaranteeing the satisfaction of constraints over continuous environments is crucial, e.g., an autonomous agent should never crash into obstacles o…
COPA: Comparing the incomparable in multi-objective model evaluation
Adrián Javaloy, Antonio Vergari, Isabel Valera
In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability…
On Faster Marginalization with Squared Circuits via Orthonormalization
Lorenzo Loconte, Antonio Vergari
Squared tensor networks (TNs) and their generalization as parameterized computational graphs -- squared circuits -- have been recently used as expressive distribution estimators in…
Is Complex Query Answering Really Complex?
Cosimo Gregucci, Bo Xiong, Daniel Hernandez +4
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be…
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
Lorenzo Loconte, Antonio Mari, Gennaro Gala +5
This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these…