Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu. Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion. To appear in CVPR 2024, Seattle, WA, USA.
Zichao Hu, Francesca Lucchetti, Claire Schlesinger, Yash Saxena, Anders Freeman, Sadanand Modak, Arjun Guha, Joydeep Biswas. Deploying and Evaluating LLMs to Program Service Mobile Robots. IEEE Robotics and Automation Letters, 2024.
Noah Patton, Kia Rahmani, Meghana Missula, Joydeep Biswas, Isil Dillig. Programming-by-Demonstration for Long-Horizon Robot Tasks. Proceedings of the ACM on Programming Languages (POPL), 2024.
Chenxi Yang, Greg Anderson, and Swarat Chaudhuri. Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation. IEEE Conference on Secure and Trustworthy Machine Learning (SatML), 2024
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alexandros G. Dimakis, Sanjay Shakkottai. Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models. NeurIPS 2023, New Orleans, LA, USA.
Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Isil Dillig, Alex Dimakis, Brighten P Godfrey, Daehyeok Kim, Christopher Rossbach, and Gang Wang. On a Foundation Model for Operating Systems. In MLSys Workshop at NeurIPS 2023, New Orleans, LA, USA.
Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman. Performance Roulette: How Cloud Weather Affects ML-Based System Optimization. In MLSys Workshop at NeurIPS 2023, New Orleans, LA, USA.
Jiayi Chen, Nihal Sharma, Tarannum Khan, Shu Liu, Brian Chang, Aditya Akella, Sanjay Shakkottai, and Ramesh Sitaraman. Darwin: Flexible Learning-based CDN Caching. SIGCOMM 2023, New York City, NY, USA.
Vipul Harsh, Wenxuan Zhou, Sachin Ashok, Radhika Niranjan Mysore, P. Brighten Godfrey, and Sujata Banerjee. Murphy: Performance Diagnosis of Distributed Cloud Applications. ACM SIGCOMM, September 2023.
Henrique Fingler, Isha Tarte, Hangchen Yu, Ariel Szekely, Bodun Hu, Aditya Akella, Christopher Rossbach. Towards a Machine Learning-Assisted Kernel with LAKE. ASPLOS 2023, Vancouver, Canada.
Greg Anderson, Swarat Chaudhuri, Isil Dillig. Guiding Safe Exploration with Weakest Preconditions. International Conference on Learning Representations (ICLR), 2023.
Yiping Ma, Jess Woods, Sebastian Angel, Antigoni Polychroniadou, and Tal Rabin. Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. S&P 2023.
Mark Mansi, Bijan Tabatabai and Michael M. Swift. CBMM: Financial Advice for Kernel Memory Managers. USENIX ATC ’22, July 2022.
Chenxi Yang and Swarat Chaudhuri. Safe Neurosymbolic Learning with Differentiable Symbolic Execution. International Conference on Learning Representations (ICLR), 2022.