CONGO: Compressive Online Gradient Optimization
Jeremy Carleton, Prathik Vijaykumar, Divyanshu Saxena, Dheeraj Narasimha, Srinivas Shakkottai, Aditya Akella.
The Thirteenth International Conference on Learning Representations, 2025.
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments.
Rohit Dwivedula, Sadanand Modak, Aditya Akella, Joydeep Biswas, Daehyeok Kim, Christopher J. Rossbach.
Preprint, January 2025.
C3: Learning Congestion Controllers with Formal Certificates.
Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula, Kshiteej Mahajan, Swarat Chaudhuri, Aditya Akella.
Preprint, December 2024.
Structural temporal logic for mechanized program verification
Eleftherios Ioannidis, Yannick Zakowski, Steve Zdancewic, Sebastian Angel
Preprint, November 2024.
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion.
Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu.
CVPR, 2024
Deploying and Evaluating LLMs to Program Service Mobile Robots.
Zichao Hu, Francesca Lucchetti, Claire Schlesinger, Yash Saxena, Anders Freeman, Sadanand Modak, Arjun Guha, Joydeep Biswas.
IEEE Robotics and Automation Letters, 2024.
Programming-by-Demonstration for Long-Horizon Robot Tasks.
Noah Patton, Kia Rahmani, Meghana Missula, Joydeep Biswas, Işil Dillig.
Proceedings of the ACM on Programming Languages (POPL), 2024.
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation.
Chenxi Yang, Greg Anderson, and Swarat Chaudhuri.
IEEE Conference on Secure and Trustworthy Machine Learning (SatML), 2024
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models.
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alexandros G. Dimakis, Sanjay Shakkottai.
NeurIPS 2023
On a Foundation Model for Operating Systems.
Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Işil Dillig, Alex Dimakis, Brighten P Godfrey, Daehyeok Kim, Christopher Rossbach, and Gang Wang.
MLSys Workshop at NeurIPS 2023
Performance Roulette: How Cloud Weather Affects ML-Based System Optimization.
Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman.
MLSys Workshop at NeurIPS 2023.
Darwin: Flexible Learning-based CDN Caching
Jiayi Chen, Nihal Sharma, Tarannum Khan, Shu Liu, Brian Chang, Aditya Akella, Sanjay Shakkottai, and Ramesh Sitaraman.
SIGCOMM 2023
Murphy: Performance Diagnosis of Distributed Cloud Applications.
Vipul Harsh, Wenxuan Zhou, Sachin Ashok, Radhika Niranjan Mysore, P. Brighten Godfrey, and Sujata Banerjee.
ACM SIGCOMM, September 2023.
Towards a Machine Learning-Assisted Kernel with LAKE.
Henrique Fingler, Isha Tarte, Hangchen Yu, Ariel Szekely, Bodun Hu, Aditya Akella, Christopher Rossbach.
ASPLOS 2023, Vancouver, Canada.
Guiding Safe Exploration with Weakest Preconditions.
Greg Anderson, Swarat Chaudhuri, Işil Dillig.
International Conference on Learning Representations (ICLR), 2023.
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning.
Yiping Ma, Jess Woods, Sebastian Angel, Antigoni Polychroniadou, and Tal Rabin.
S&P 2023.
CBMM: Financial Advice for Kernel Memory Managers.
Mark Mansi, Bijan Tabatabai and Michael M. Swift.
USENIX ATC ’22, July 2022.
Safe Neurosymbolic Learning with Differentiable Symbolic Execution.
Chenxi Yang and Swarat Chaudhuri.
International Conference on Learning Representations (ICLR), 2022.