| 2021 | ICML | Differentially Private Bayesian Inference for Generalized Linear Models. | Tejas D. Kulkarni, Joonas Jlk, Antti Koskela, Samuel Kaski, Antti Honkela |
| 2019 | ICLR | Unsupervised Control Through Non-Parametric Discriminative Rewards. | David Warde-Farley, Tom Van de Wiele, Tejas D. Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih |
| 2017 | CVPR | Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks. | Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D. Kulkarni, Joshua B. Tenenbaum |
| 2017 | ICLR | Learning to Perform Physics Experiments via Deep Reinforcement Learning. | Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas |
| 2015 | CogSci | 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 |
| 2015 | CVPR | Picture: A probabilistic programming language for scene perception. | Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka |
| 2015 | EMNLP | Language Understanding for Text-based Games using Deep Reinforcement Learning. | Karthik Narasimhan, Tejas D. Kulkarni, Regina Barzilay |