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...
Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood. Recent work regularizes this trace globally to bias learning toward flatter, better generalizi...
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
A unified perspective on the inherent trade-offs between expressivity and tractability is provided, highlighting the design principles and algorithmic extensions that have enabled building expressive and efficient PCs, and a taxonomy of the field is provided.
Sahil Sidheekh, S. Natarajan· International Joint Conferen...· 13 citations
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