Arya Mazumdar
Professor
Works on information theory and statistical learning, including coding theory, clustering and distributed learning.
Arya Mazumdar's lab at UCSD works on learning theory & optimization, signal processing & information theory and distributed, cloud & edge systems. 42 papers since 2023, 3 at top venues, mostly JMLR and Journal of the ACM. 7 PhD students. 12 alumni. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 7%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 12
Papers by yearfrom the lab homepage, 2023–2026
- 11 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 0
- 11 papers2024 · T1 2
- 8 papers2023 · T1 1
T1 venuesOtherVenues: JMLR, Journal of the ACM, Machine Learning
Research focuspaper titles that use each topic's words
- Learning theory & optimization14 papers
- Distributed, cloud & edge systems6 papers
- Signal processing & information theory5 papers
- clustering4 papers
- coding theory0 papers
Paper list
From the lab homepage.
202611 papers
- Conference on Learning Theory (COLT), .Actively Learning Halfspaces without Synthetic Data (opens in a new tab)
- IEEE BITS the Information Theory Magazine, +.Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching (opens in a new tab)
- Algorithmica, Springer, +.Clustering with Non-adaptive Subset Queries (opens in a new tab)
- TSPDistributed Hybrid Sketching for ℓ2-Embeddings (opens in a new tab)
- International Conference on Machine Learning (ICML), .Fairness in Aggregation: Optimal Top-k and Improved Full Ranking (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design (opens in a new tab)
- Machine LearningImproving Selective Classification with Pairwise Queries for Binary Classification (opens in a new tab)
- International Conference on Machine Learning (ICML), .On the Theory of Continual Learning with Gradient Descent for Neural Networks (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Rare-Tail Statistics For Learning Biased Gaussian Halfspaces with Label Noise
- IEEE Journal on Selected Areas of Information Theory (JSAIT), Data Compression special issue, .Support Recovery in One-bit Compressed Sensing with Near-Optimal Measurements and Sublinear Time (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .The Noisy Quantitative Group Testing Problem (opens in a new tab)
202512 papers
- Transactions on Machine Learning Research (TMLR), Oct .Collaborative Compressors in Distributed Mean Estimation with Limited Communication Budget (opens in a new tab)
- International Conference on Machine Learning (ICML), .Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .Learning and Generalization With Mixture Data (opens in a new tab)
- Conference on Learning Theory (COLT), .Learning Partitions with Optimal Query and Round Complexities (opens in a new tab)
- Conference on Learning Theory (COLT), .Learning sparse generalized linear models with binary outcomes via iterative hard thresholding (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .Noisy Nonadaptive Group Testing with Binary Splitting: New Test Design and Improvement on Price-Scarlett-Tan's Scheme (opens in a new tab)
- Conference on Learning Theory (COLT), .Optimal Graph Reconstruction by Counting Connected Components in Induced Subgraphs (opens in a new tab)
- International Conference on Machine Learning (ICML), .Optimal Transfer Learning for Missing Not-at-Random Matrix Completion (opens in a new tab)
- TITRobust Distributed Clustering with Redundant Data Assignment (opens in a new tab)
- International Conference on Learning Representations (ICLR), .Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods (opens in a new tab)
- TITSupport Recovery in Mixture Models with Sparse Parameters (opens in a new tab)
202411 papers
- International Conference on Machine Learning (ICML), .Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms (opens in a new tab)
- Transactions on Machine Learning Research (TMLR), Mar , .An Improved Federated Clustering Algorithm with Model-based Clustering (opens in a new tab)
- Journal of the ACM (T1)Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing (opens in a new tab)
- TITCompeting Bandits in Non-Stationary Matching Markets (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .DIST-CURE: A Robust Distributed Learning Algorithm with Cubic Regularized Newton (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .Distributed Local Sketching for l2 Embeddings
- TITImproved Support Recovery in One-Bit Compressed Sensing (opens in a new tab)
- The Annual Conference on Learning Theory (COLT), .On the sample complexity of parameter estimation in logistic regression with normal design (opens in a new tab)
- JMLR (T1)Random Subgraph Detection Using Queries (opens in a new tab)
- ACM/SIAM Symposium on Discrete Algorithms (SODA), Jan .Robust 1-bit Compressed Sensing with Iterative Hard Thresholding (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), .Transfer Learning for Latent Variable Network Models (opens in a new tab)
20238 papers
- ICML WorkshopA Convergent Federated Clustering Algorithm without Initial Condition (opens in a new tab)
- Also in (W) ICLR Workshop on Machine Learning for IoT, .Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks (opens in a new tab)
- IEEE Journal on Selected Areas of Information Theory (JSAIT), Special Alexander Vardy Memorial IssueCapacity of Locally Recoverable Codes (opens in a new tab)
- JMLR (T1)Community Recovery in the Geometric Block Model (opens in a new tab)
- NeurIPS WorkshopConsensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization (opens in a new tab)
- ICML WorkshopDecentralized Competing Bandits in Non-Stationary Matching Markets (opens in a new tab)
- ICML WorkshopFED-CURE: A Robust Federated Learning Algorithm with Cubic Regularized Newton (opens in a new tab)
- IEEE International Symposium on Information Theory (ISIT), .Optimal Compression of Unit Norm Vectors in the High Distortion Regime (opens in a new tab)
Where alumni went12 listed
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
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