1 citations · 1 across the 11 of their papers we have counts for
12 papers
PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails
Satchit Chatterji, Shihan Wang, Giovanni Sileno +1
Large language model guardrails can be viewed as policy-consistency problems: a system must determine which policy-relevant facts hold in a prompt-response pair and what those fact…
How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making
Adia Lumadjeng, Ilker Birbil, Erman Acar
In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose by producing formulas with do…
Explaining the Explainer: Understanding the Inner Workings of Transformer-based Symbolic Regression Models
Arco van Breda, Erman Acar
Following their success across many domains, transformers have also proven effective for symbolic regression (SR); however, the internal mechanisms underlying their generation of m…
ECSEL: Explainable Classification via Signomial Equation Learning
Adia Lumadjeng, Ilker Birbil, Erman Acar
We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regressi…
Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs
Angela van Sprang, Laurens Samson, Ana Lucic +3
We introduce two new benchmarks REST and REST+ (Render-Equivalence Stress Tests) to enable systematic evaluation of cross-modal inconsistency in multimodal large language models (M…
Successful Misunderstandings: Learning to Coordinate Without Being Understood
Nikolaos Kondylidis, Anil Yaman, Frank van Harmelen +2
The main approach to evaluating communication is by assessing how well it facilitates coordination. If two or more individuals can coordinate through communication, it is generally…
Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations
Linh Nguyen, Marcel Boersma, Erman Acar
Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzin…
EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets
Mayesha Tasnim, Erman Acar, Sennay Ghebreab
The design of fair and efficient algorithms for allocating public resources, such as school admissions, housing, or medical residency, has a profound social impact. In one-sided ma…
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning
Satchit Chatterji, Erman Acar
Safe reinforcement learning (RL) is crucial for real-world applications, and multi-agent interactions introduce additional safety challenges. While Probabilistic Logic Shields (PLS…
Integrating Fuzzy Logic into Deep Symbolic Regression
Wout Gerdes, Erman Acar
Credit card fraud detection is a critical concern for financial institutions, intensified by the rise of contactless payment technologies. While deep learning models offer high acc…
Differentiable Inductive Logic Programming for Fraud Detection
Boris Wolfson, Erman Acar
Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of…
Interpretability for Time Series Transformers using A Concept Bottleneck Framework
Angela van Sprang, Erman Acar, Willem Zuidema
Mechanistic interpretability focuses on reverse engineering the internal mechanisms learned by neural networks. We extend our focus and propose to mechanistically forward engineer…