ADITYA BHASKARA portrait
  • Associate Professor, School Of Computing

Publications

  • Aditya Bhaskara & Aidao Chen, Aidan Perreault, Aravindan Vijayaraghavan (2022). Smoothed analysis for tensor methods in unsupervised learning. Mathematical Programming Series B. Vol. 193, 549-599. Published, 06/2022.
  • Frost Mitchell & Aniqua Baset, Neal Patwari, Sneha Kumar Kasera, Aditya Bhaskara (2022). Deep Learning-based Localization in Limited Data Regimes. WiseML Workshop at WiSec 2022 (small but refereed workshop). Published, 05/2022.
  • Aditya Bhaskara & Ashok Cutkosky, Ravi Kumar, Manish Purohit (2021). Logarithmic Regret from Sublinear Hints. Neural Information Processing Systems. Published, 12/2021.
  • Maheshakya Wijewardena & A. Bhaskara, K. Ruwanpathirana (2021). Additive Error Guarantees for Weighted Low Rank Approximation. ICML (International Conference on Machine Learning). Published, 07/2021.
  • Mohsen Abbassi & A. Bhaskara, S. Venkatasubramanian (2021). Fair Clustering via Equitable Group Representations. ACM (Conference on Fairness, Accountability and Transparency). Published, 04/01/2021.
  • Aditya Bhaskara & A. Cutkosky, R. Kumar, M. Purohit (2021). Power of Hints for Online Learning with Movement Costs. AISTATS (International Conference on Artificial Intelligence and Statistics). Published, 04/01/2021.
  • Kanchana Ruwanapathirana & A. Bhaskara, M. Wijewardena (2021). Principal Component Regression with Semirandom Observations via Matrix Completion. AISTATS (International Conference on Artificial Intelligence and Statistics). Published, 04/01/2021.
  • Aditya Bhaskara & A. Karbasi, S. Lattanzi, M. Zadimoghaddam (2020). Online MAP Inference of Determinantal Point Processes. Neural Information Processing Systems. Published, 12/01/2020.
  • Aditya Bhaskara & K. Munagala, S. Gollapudi, K. Kollias (2020). Adaptive Probing Policies for Shortest Path Routing. Neural Information Processing Systems. Published, 12/01/2020.
  • Aditya Bhaskara & A. Cutkosky, R. Kumar, M. Purohit (2020). Online Linear Optimization with Many Hints. Neural Information Processing Systems. Published, 12/01/2020.
  • Maheshakya Wijewardena & A. Bhaskara, S. K. Kasera, S. A. Mahmud, N. Patwari (2020). A Plug-n-Play Game Theoretic Framework For Defending Against Radio Window Attack. ACM. 10. Published, 07/15/2020.
  • Aditya Bhaskara (2020). Online Learning with Imperfect Hints. Proceedings of the 37th International Conference on Machine Learning. Published, 06/2020.
  • Kanchana Ruwanpathirana (2020). Robust Algorithms for Online k-means Clustering. Proceedings of the 31st International Conference on Algorithmic Learning Theory. Published, 02/2020.
  • Maheshakya Wijewardena (2019). On Distributed Averaging for Stochastic k-PCA. Advances in Neural Information Processing Systems. Published, 12/2019.
  • Aditya Bhaskara (2019). Greedy Sampling for Approximate Clustering in the Presence of Outliers. Advances in Neural Information Processing Systems. Published, 12/2019.
  • Aditya Bhaskara & Silvio Lattanzi, Morteza Zadimoghaddam and Sergei Vassilvitskii (2019). Residual Based Sampling for Online Low Rank Approximtion. IEEE Foundations of Computer Science. Published, 11/2019.
  • Aditya Bhaskara & Aidao Chen, Aidan Perrault and Aravindan Vijayaraghavan (2019). Smoothed Analysis in Unsupervised Learning via Decoupling. IEEE Foundations of Computer Science. Published, 11/2019.
  • Aniqua Baset & Christopher Becker, Kurt Derr, Samuel Ramirez, Sneha Ku- mar Kasera, Aditya Bhaskara (2019). Towards Wireless Environment Cognizance by Incremental Learning. IEEE. Published, 11/2019.
  • Aditya Bhaskara & Antoine Vigneron (2019). Approximating a planar convex set using a sparse grid. Elsevier, Information Processing Letters. Published, 09/2019.
  • Aditya Bhaskara & Wai Ming Tai (2019). Approximate Guarantees for Dictionary Learning. Conference on Learning Theory. Published, 06/2019.
  • Anuj Dhimri, Harsimran Singh, Shamik Sarkar, Sneha Kasera, Neal Patwari, Aditya Bhaskara, Kurt Derr & Samuel Ramirez (2018). Privacy Enabled Noise Free Data Collection in Vehicular Networks. IEEE Mobile Ad Hoc and Sensor Systems (MASS). Published, 10/2018.
  • Aditya Bhaskara & Srivatsan Kumar (2018). Low Rank Approximation in the Presence of Outliers. International Conference on Approximation Algorithms for Combinatorial Optimization Problems. Published, 08/2018.
  • Aditya Bhaskara & Maheshakya Wijewardena (2018). Distributed Clustering via LSH Based Data Partitioning. International Conference on Machine Learning. Published, 07/2018.
  • Aditya Bhaskara, Samira Daruki & Suresh Venkatasubramanian (2018). Sublinear Algorithms for MAXCUT and Correlation Clustering. International Colloquium on Automata, Languages, and Programming. Published, 07/2018.
  • Aditya Bhaskara & Silvio Lattanzi (2018). Non-negative Sparse Regression and Column Selection with L1 Error. Innovations in Theoretical Computer Science (ITCS). Published, 01/2018.
  • Aditya Bhaskara & Felix Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang (2017). On Binary Embedding using Circulant Matrices. Journal of Machine Learning Research. Published, 12/2017.
  • Linear Relaxations for Finding Diverse Elements in Metric Spaces (with Mehrdad Ghadiri, Vahab Mirrokni and Ola Svensson) To appear in Advances in Neural Information Processing Systems (NIPS 2016). Published, 12/06/2016.
    https://papers.nips.cc/paper/6500-linear-relaxatio...
  • Greedy Column Subset Selection: New Bounds and Distributed Algorithms (with Jason Altschuler, Gang (Thomas) Fu, Vahab Mirrokni, Afshin Rostamizadeh and Morteza Zadimoghaddam) Proceedings of the 33rd International Conference on Machine Machine Learning (ICML 2016). Published, 06/15/2016.
    http://www.cs.utah.edu/~bhaskara/files/greedycss.p...
  • Expanders via Local Edge Flips, (with Zeyuan Allen-Zhou, Silvio Lattanzi, Vahab Mirrokni and Lorenzo Orecchia) Proceedings of the 27th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2016). Published, 01/06/2016.
    http://www.cs.utah.edu/~bhaskara/files/flipchain.p...

Research Keywords

  • Theoretical computer science
  • Machine learning
  • Graph algorithms
  • Approximation algorithms

Presentations

  • IISc/MSR Joint Seminar Series. Invited Talk/Keynote, Presented, 03/2022.
  • National Institute of Technology Calicut (India). Invited Talk/Keynote, Presented, 10/2020.
  • Workshop on Information Theory and Applications (UCSD). Invited Talk/Keynote, Presented, 02/2020.
  • IEEE Conference on the Foundations of Computer Science (FOCS) Talk title: Residual Based Sampling for Online Low Rank Approximation. Conference Paper, Refereed, Presented, 11/2019.
  • Low Rank Approximation in the Presence of Outliers, Stanford University. Invited Talk/Keynote, Presented, 10/2018.
  • Non-negative Sparse Regression and Column Selection with L1 Error. Conference Paper, Refereed, Presented, 01/2018.
  • Greedy algorithms for column selection, Information Theory and Applications (ITA). Invited Talk/Keynote, Presented, 02/2017.
  • Expanders via Local Edge Flips. Invited Talk/Keynote, Presented, 06/15/2016.

Research Groups

  • Prasanth Yalamanchili, Graduate Student. School of Computing. 08/2022 - present.
  • Christopher Neal Harker, Graduate Student. 08/15/2020 - present.
  • Frost Mitchell, Graduate Student. 08/2020 - present.
  • Kanchana Ruwanpathirana, Graduate Student. SoC. 09/2018 - present.
  • Scott Neville, Undergraduate Student. 01/2018 - 07/2018.
  • Srivatsan Kumar, Graduate Student. 01/2017 - 06/2018.