Applying mobile device soft keyboards to collaborative multitouch tabletop displays: design and evaluation. - Sungahn Ko; KyungTae Kim; Tejas D. Kulkarni; Niklas Elmqvist (ITS, 2011)

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. - Vikash K. Mansinghka; Tejas D. Kulkarni; Yura N. Perov; Joshua B. Tenenbaum (NIPS, 2013)

Inverse Graphics with Probabilistic CAD Models. - Tejas D. Kulkarni; Vikash K. Mansinghka; Pushmeet Kohli; Joshua B. Tenenbaum (CoRR, 2014)

Variational Particle Approximations. - Tejas D. Kulkarni; Ardavan Saeedi; Samuel Gershman (CoRR, 2014)

Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. - Ilker Yildirim; Tejas D. Kulkarni; Winrich Freiwald; Joshua B. Tenenbaum (CogSci, 2015)

Picture: A probabilistic programming language for scene perception. - Tejas D. Kulkarni; Pushmeet Kohli; Joshua B. Tenenbaum; Vikash Mansinghka 001 (CVPR, 2015)

Language Understanding for Text-based Games using Deep Reinforcement Learning. - Karthik Narasimhan; Tejas D. Kulkarni; Regina Barzilay (EMNLP, 2015)

Deep Convolutional Inverse Graphics Network. - Tejas D. Kulkarni; William F. Whitney; Pushmeet Kohli; Joshua B. Tenenbaum (NIPS, 2015)

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. - Tejas D. Kulkarni; Karthik Narasimhan; Ardavan Saeedi; Josh Tenenbaum 001 (NIPS, 2016)

Deep Successor Reinforcement Learning. - Tejas D. Kulkarni; Ardavan Saeedi; Simanta Gautam; Samuel J. Gershman (CoRR, 2016)

Understanding Visual Concepts with Continuation Learning. - William F. Whitney; Michael Chang 003; Tejas D. Kulkarni; Joshua B. Tenenbaum (CoRR, 2016)

Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks. - Amir Arsalan Soltani; Haibin Huang; Jiajun Wu 001; Tejas D. Kulkarni; Joshua B. Tenenbaum (CVPR, 2017)

Learning to Perform Physics Experiments via Deep Reinforcement Learning. - Misha Denil; Pulkit Agrawal 001; Tejas D. Kulkarni; Tom Erez; Peter W. Battaglia; Nando de Freitas (ICLR (Poster), 2017)

Self-Supervised Intrinsic Image Decomposition. - Michael Janner; Jiajun Wu 001; Tejas D. Kulkarni; Ilker Yildirim; Josh Tenenbaum 001 (NIPS, 2017)

Variational Particle Approximations. - Ardavan Saeedi; Tejas D. Kulkarni; Vikash K. Mansinghka; Samuel J. Gershman (J. Mach. Learn. Res., 2017)

Unsupervised Control Through Non-Parametric Discriminative Rewards. - David Warde-Farley; Tom Van de Wiele; Tejas D. Kulkarni; Catalin Ionescu; Steven Hansen 001; Volodymyr Mnih (ICLR (Poster), 2019)

Unsupervised Learning of Object Keypoints for Perception and Control. - Tejas D. Kulkarni; Ankush Gupta 001; Catalin Ionescu; Sebastian Borgeaud; Malcolm Reynolds; Andrew Zisserman; Volodymyr Mnih (NeurIPS, 2019)

Practical Differentially Private Hyperparameter Tuning with Subsampling. - Antti Koskela; Tejas D. Kulkarni (NeurIPS, 2023)

Communication Efficient Differentially Private Federated Learning Using Second Order Information. - Mounssif Krouka; Antti Koskela; Tejas D. Kulkarni (Proc. Priv. Enhancing Technol., 2025)

Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models. - Antti Koskela; Tejas D. Kulkarni (CoRR, 2025)