Publications

Effective, Efficient, and Explainable Learning in Data-Scarce and Noisy Domains

On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift

Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach

A Unified Framework for Human-Allied Learning of Probabilistic Circuits

Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes

Orient: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift

Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes

Test-time Active Feature Selection through Tractable Acquisition Functions

Bayesian Learning of Probabilistic Circuits with Domain Constraints

Deep Tractable Probabilistic Models