Publications
Research papers and publications in AI, machine learning, and related fields.
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2026
Tractable Representation Learning with Probabilistic Circuits
Steven Braun, Sahil Sidheekh, Antonio Vergari, Marius Mundt, Sriraam Natarajan, Kristian Kersting
Tractable Sharpness-Aware Learning of Probabilistic Circuits
AAAI '26H Suresh, Sahil Sidheekh, S Natarajan, NC Krishnan
2025
Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
AAAI '25S Natarajan, S Mathur, Sahil Sidheekh, W Stammer, K Kersting
Credibility-aware multi-modal fusion using probabilistic circuits
AISTATS '25Sahil Sidheekh, P Tenali, S Mathur, E Blasch, K Kersting, S Natarajan
A Unified Framework for Human-Allied Learning of Probabilistic Circuits
AAAI '25Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
Autoencoding Probabilistic Circuits
TPM Workshop '25S Braun, Sahil Sidheekh, S Natarajan, A Vergari, M Mundt, K Kersting
Human-Allied Relational Reinforcement Learning
ACS '25FG Darvishvand, H Shindo, Sahil Sidheekh, K Kersting, S Natarajan
2024
Building Expressive and Tractable Probabilistic Generative Models: A Review
IJCAI '24Sahil Sidheekh, S Natarajan
On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits
FUSION '24Sahil Sidheekh, P Tenali, S Mathur, E Blasch, S Natarajan
Credibility-aware Reliable Multi-Modal Fusion Using Probabilistic Circuits
AAAI '24S Mathur, Sahil Sidheekh, P Tenali, E Blasch, K Kersting, S Natarajan
2023
EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models
DA O'Brien, S Deb, Sahil Sidheekh, NC Krishnan, P Sharathi Dutta, et al.
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference
UAI '23Sahil Sidheekh, K Kersting, S Natarajan
Leveraging task variability in meta-learning
A Aimen, B Ladrecha, Sahil Sidheekh, NC Krishnan
Adaptation: Blessing or Curse for Higher Way Meta-Learning
A Aimen, Sahil Sidheekh, B Ladrecha, H Ahuja, NC Krishnan
Bayesian learning of probabilistic circuits with domain constraints
TPM Workshop '23A Karanam, S Mathur, Sahil Sidheekh, S Natarajan
2022
Machine learning methods trained on simple models can predict critical transitions in complex natural systems
S Deb, Sahil Sidheekh, CF Clements, NC Krishnan, PS Dutta
VQ-Flows: Vector Quantized Local Normalizing Flows
UAI '22Sahil Sidheekh, CB Dock, T Jain, R Balan, MK Singh
2021
On Characterizing GAN Convergence Through Proximal Duality Gap
ICML '21Sahil Sidheekh, A Aimen, NC Krishnan
Task Attended Meta-Learning for Few-Shot Learning
NeurIPS '21A Aimen, Sahil Sidheekh, NC Krishnan
