| 2025 | ICCV | From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations. | Anthony Bisulco, Rahul Ramesh, Randall Balliestro, Pratik Chaudhari |
| 2025 | ICLR | AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents. | Ke Yang, Yao Liu, Sapana Chaudhary, Rasool Fakoor, Pratik Chaudhari, George Karypis, Huzefa Rangwala |
| 2025 | ICRA | An Active Perception Game for Robust Information Gathering. | Siming He, Yuezhan Tao, Igor Spasojevic, Vijay Kumar, Pratik Chaudhari |
| 2024 | ICLR | Time-Varying Propensity Score to Bridge the Gap between the Past and Present. | Rasool Fakoor, Jonas Mueller, Zachary Chase Lipton, Pratik Chaudhari, Alex Smola |
| 2024 | ICRA | TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards. | Derek Cheng, Fernando Cladera Ojeda, Ankit Prabhu, Xu Liu, Alan Zhu, Patrick Corey Green, Reza Ehsani, Pratik Chaudhari, Vijay Kumar |
| 2024 | IROS | Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments. | Christopher D. Hsu, Pratik Chaudhari |
| 2024 | ICRA | Design and Evaluation of Motion Planners for Quadrotors in Environments with Varying Complexities. | Yifei Simon Shao, Yuwei Wu, Laura Jarin-Lipschitz, Pratik Chaudhari, Vijay Kumar |
| 2023 | CVPR | Beyond mAP: Towards Better Evaluation of Instance Segmentation. | Rohit Jena, Lukas Zhornyak, Nehal Doiphode, Pratik Chaudhari, Vivek Buch, James C. Gee, Jianbo Shi |
| 2023 | ICML | A Picture of the Space of Typical Learnable Tasks. | Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari |
| 2023 | ICML | The Value of Out-of-Distribution Data. | Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein |
| 2022 | AAAI | Does the Geometry of the Data Control the Geometry of Neural Predictions? (Student Abstract). | Anirudh Cowlagi, Pratik Chaudhari |
| 2022 | ICLR | Model Zoo: A Growing Brain That Learns Continually. | Rahul Ramesh, Pratik Chaudhari |
| 2022 | ICML | Deep Reference Priors: What is the best way to pretrain a model? | Yansong Gao, Rahul Ramesh, Pratik Chaudhari |
| 2022 | ICML | Does the Data Induce Capacity Control in Deep Learning? | Rubing Yang, Jialin Mao, Pratik Chaudhari |
| 2021 | ICML | An Information-Geometric Distance on the Space of Tasks. | Yansong Gao, Pratik Chaudhari |
| 2021 | ICRA | MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation. | Xiaoyi Chen, Pratik Chaudhari |
| 2021 | IROS | Scalable Reinforcement Learning Policies for Multi-Agent Control. | Christopher D. Hsu, Heejin Jeong, George J. Pappas, Pratik Chaudhari |
| 2021 | ICRA | Deformable Linear Object Prediction Using Locally Linear Latent Dynamics. | Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari, Kostas Daniilidis |
| 2021 | MICCAI | Harmonization with Flow-Based Causal Inference. | Rongguang Wang, Pratik Chaudhari, Christos Davatzikos |
| 2020 | CoRL | BayesRace: Learning to race autonomously using prior experience. | Achin Jain, Matthew O'Kelly, Pratik Chaudhari, Manfred Morari |
| 2020 | ICLR | A Baseline for Few-Shot Image Classification. | Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto |
| 2020 | ICLR | Meta-Q-Learning. | Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola |
| 2020 | ICLR | Rethinking the Hyperparameters for Fine-tuning. | Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto |
| 2020 | ICML | A Free-Energy Principle for Representation Learning. | Yansong Gao, Pratik Chaudhari |
| 2020 | IROS | Proximal Deterministic Policy Gradient. | Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari |
| 2019 | UAI | P3O: Policy-on Policy-off Policy Optimization. | Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola |
| 2018 | ICLR | Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks. | Pratik Chaudhari, Stefano Soatto |
| 2018 | ITA | Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks. | Pratik Chaudhari, Stefano Soatto |
| 2017 | ACSSC | Partial differential equations for training deep neural networks. | Pratik Chaudhari, Adam M. Oberman, Stanley J. Osher, Stefano Soatto, Guillaume Carlier |
| 2017 | ICLR | Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. | Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina |
| 2014 | ICRA | Sampling-based algorithms for optimal motion planning using process algebra specifications. | Valerio Varricchio, Pratik Chaudhari, Emilio Frazzoli |
| 2014 | ICRA | Game theoretic controller synthesis for multi-robot motion planning Part I: Trajectory based algorithms. | Minghui Zhu, Michael W. Otte, Pratik Chaudhari, Emilio Frazzoli |