Does this App Really Need My Location?: Context-Aware Privacy Management for Smartphones. - Saksham Chitkara; Nishad Gothoskar; Suhas Harish; Jason I. Hong; Yuvraj Agarwal (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017)

Learning higher-order sequential structure with cloned HMMs. - Antoine Dedieu; Nishad Gothoskar; Scott Swingle; Wolfgang Lehrach; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2019)

Learning a generative model for robot control using visual feedback. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Abhishek Agarwal; Yasemin Bekiroglu; Dileep George (CoRR, 2020)

From proprioception to long-horizon planning in novel environments: A hierarchical RL model. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2020)

Query Training: Learning and inference for directed and undirected graphical models. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (CoRR, 2020)

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (AAAI, 2021)

3DP3: 3D Scene Perception via Probabilistic Programming. - Nishad Gothoskar; Marco F. Cusumano-Towner; Ben Zinberg; Matin Ghavamizadeh; Falk Pollok; Austin Garrett; Josh Tenenbaum 001; Dan Gutfreund; Vikash K. Mansinghka (NeurIPS, 2021)

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Yasemin Bekiroglu; Abhishek Agarwal; Joshua B. Tenenbaum; Vikash K. Mansinghka; Dileep George (ICRA, 2022)

Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. - Tan Zhi-Xuan; Nishad Gothoskar; Falk Pollok; Dan Gutfreund; Joshua B. Tenenbaum; Vikash K. Mansinghka (CoRR, 2022)

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. - Alexander K. Lew; George Matheos; Tan Zhi-Xuan; Matin Ghavamizadeh; Nishad Gothoskar; Stuart Russell 001; Vikash K. Mansinghka (AISTATS, 2023)

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (ICCV, 2023)

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (CoRR, 2023)

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. - Nishad Gothoskar; Matin Ghavami; Eric Li; Aidan Curtis; Michael Noseworthy; Karen Chung; Brian Patton; William T. Freeman; Joshua B. Tenenbaum; Mirko Klukas; Vikash K. Mansinghka (CoRR, 2023)

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. - Aidan Curtis; George Matheos; Nishad Gothoskar; Vikash Mansinghka 001; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (Robotics: Science and Systems, 2024)

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills. - Aidan Curtis; Eric Li; Michael Noseworthy; Nishad Gothoskar; Sachin Chitta; Hui Li; Leslie Pack Kaelbling; Nicole Carey (CoRR, 2025)