Raghavendra Addanki
I am a Research Scientist at Adobe Research (San Jose, California). I completed my Ph.D. in Computer Science from the College of Information and Computer Sciences at the University of Massachusetts Amherst in May 2022, where I was co-advised by Prof. Andrew McGregor and Prof. Cameron Musco. I received a Dissertation Writing Fellowship for my Ph.D. thesis. I was a visiting student in the Causality program at the Simons Institute for the Theory of Computing, Berkelely in 2022. Prior to this, I completed my Bachelors and Masters degrees (dual degree) in Computer Science from the Indian Institute of Technology Madras in 2016.
Contact Email: raddanki AT adobe.com
Internships: If you are a Ph.D. student looking for internship opportunities for Summer 2025, please apply here and send me an email with your research interests.
Research Interests
I am broadly interested in the design and analysis of algorithms for data science, causal inference, theoretical computer science, and machine learning. During my Ph.D., among other things, I have worked on identifying new connections between combinatorial optimization and causal inference.
Publications (author ordering for the papers below is alphabetical unless marked *)
Limits of Approximating the Median Treatment Effect
Raghavendra Addanki and Siddharth Bhandari
Conference on Learning Theory (COLT) 2024.
[arXiv, proceedings]Continuous Treatment Effects with Surrogate Outcomes*
Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan Rossi, Ritwik Sinha, and Edward H. Kennedy
International Conference on Machine Learning (ICML) 2024.
[arXiv]Causal Discovery-Driven Change Point Detection in Time Series*
Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, and Murat Kocaoglu
[arXiv]One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space
Raghavendra Addanki, Chenyang Li, Zhao Song, Chiwun Yang
[arXiv]Parameter-Efficient Fine-Tuning via Partially Decomposable Loss Analysis and Sharing
Raghavendra Addanki, Ritwik Sinha, Zhao Song, Yizhou Wang, Lichen Zhang
[pdf]Causal Discovery in Semi-Stationary Time Series*
Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, and Murat Kocaoglu
Neural Information Processing Systems (NeurIPS) 2023.
[arXiv, proceedings]Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform*
Akash Maharaj, Ritwik Sinha, David Arbour, Ian Waudby-Smith, Simon Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg and Viswanathan Swaminathan
ACM Web Conference (WWW), Industry Track, 2023.
[arXiv, proceedings]Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, and Anup Rao
Conference on Neural Information Processing Systems (NeurIPS) 2022.
[arXiv, proceedings, code]Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries
Raghavendra Addanki, Andrew McGregor, and Cameron Musco
European Symposium on Algorithms (ESA) 2022.
[arXiv, proceedings]Improved Approximation and Scalability for Fair Max-Min Diversification
Raghavendra Addanki, Andrew McGregor, Alexandra Meliou, and Zafeiria Moumoulidou
International Conference on Database Theory (ICDT) 2022.
[arXiv, proceedings]Collaborative Causal Discovery with Atomic Interventions
Raghavendra Addanki, Shiva Prasad Kasiviswanathan
Conference on Neural Information Processing Systems (NeurIPS) 2021.
[arXiv, proceedings][12 min video at NeurIPS]Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Raghavendra Addanki, Andrew McGregor, and Cameron Musco
International Conference on Algorithmic Learning Theory (ALT) 2021.
[arXiv, proceedings][1hr video at MIT, 12 min video at ALT]How to Design Robust Algorithms using Noisy Comparison Oracle
Raghavendra Addanki, Sainyam Galhotra, and Barna Saha
International Conference on Very Large Data Bases (VLDB) 2021.
[arXiv, proceedings]Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, and Cameron Musco
International Conference on Machine Learning (ICML) 2020.
[arXiv, proceedings][15 min video at ICML, 1 hr video at NUS]Search Result Diversification with Guarantee of Topic Proportionality*
Sheikh Muhammad Sarwar, Raghavendra Addanki, Ali Montazeralghaem, Soumyabrata Pal, and James Allan
International Conference on the Theory of Information Retrieval (ICTIR) 2020.
[proceedings]Dynamic Set Cover: Improved Algorithms and Lower Bounds
Amir Abboud, Raghavendra Addanki, Fabrizio Grandoni, Debmalya Panigrahi, and Barna Saha
Symposium on Theory of Computing (STOC) 2019.
[proceedings]Embed as you need: Evaluation of Random Walk and Poincare Embeddings for Healthcare Tasks*
Khushbu Agarwal, Tome Eftimov, Raghavendra Addanki, Sutanay Choudhury, Suzanne Tamang, and Robert Rallo
Workshop on Applied Data Science for Healthcare, Knowledge Discovery and Data Mining (KDD) 2019.
[arXiv]
Miscellaneous
Theoretical Computer Science group at UMass Amherst. There is a weekly theory seminar every semester – if you are interested in giving a talk, please let one of the organizers know.