Author

Saurabh Mathur

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#artificial intelligence Preprint Oct 2026

Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

Declarative logic programs offer a powerful and interpretable abstraction for encoding relational structure and neurosymbolic reasoning, by expressing dependencies as weighted compositional rules. However, inducing them from data remains fundamentally hard, bottlenecked by the combinatorial explosion of symbolic search...

Saurabh Mathur, Sahil Sidheekh, Bhavan Kumar Vasu et al. · 0 citations

Preprint Aug 2026

Tydra: An Efficient Hybrid Model for Tabular Data

Tydra is introduced, a hybrid Transformer-State Space Model (SSM) architecture for tabular in-context learning that interleaves attention and SSM layers that reduces inference time by 30% relative to TabPFN while retaining much of its predictive performance.

Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus et al. · 1 citation

#machine learning Preprint Aug 2026

Reliable Fusion of Conflicting Experts

We study the problem of aggregating opinions from multiple black-box experts in noisy, conflict-prone settings where expert reliability varies across inputs. Static aggregation methods, such as majority voting, fail to capture this variability and often yield unreliable outcomes under disagreement. We propose a tractab...

Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur et al. · 0 citations