| 2026 | COLT | Open Problem: How much overparametrization is needed for ALS in tensor decomposition? | Dionysis Arvanitakis, Vaidehi Srinivas, Aravindan Vijayaraghavan |
| 2026 | COLT | Low-Degree Method Fails to Predict Robust Subspace Recovery. | He Jia, Aravindan Vijayaraghavan |
| 2025 | COLT | Computing High-dimensional Confidence Sets for Arbitrary Distributions. | Chao Gao, Liren Shan, Vaidehi Srinivas, Aravindan Vijayaraghavan |
| 2025 | COLT | Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals. | Anxin Guo, Aravindan Vijayaraghavan |
| 2025 | ICML | Volume Optimality in Conformal Prediction with Structured Prediction Sets. | Chao Gao, Liren Shan, Vaidehi Srinivas, Aravindan Vijayaraghavan |
| 2024 | FOCS | Efficient Certificates of Anti-Concentration Beyond Gaussians. | Ainesh Bakshi, Pravesh K. Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan |
| 2024 | SODA | Higher-Order Cheeger Inequality for Partitioning with Buffers. | Konstantin Makarychev, Yury Makarychev, Liren Shan, Aravindan Vijayaraghavan |
| 2024 | STOC | New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries. | Aditya Bhaskara, Eric Evert, Vaidehi Srinivas, Aravindan Vijayaraghavan |
| 2023 | FOCS | Computing linear sections of varieties: quantum entanglement, tensor decompositions and beyond. | Nathaniel Johnston, Benjamin Lovitz, Aravindan Vijayaraghavan |
| 2023 | ICLR | Agnostic Learning of General ReLU Activation Using Gradient Descent. | Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan |
| 2022 | ALT | Understanding Simultaneous Train and Test Robustness. | Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan |
| 2022 | ALT | Algorithms for learning a mixture of linear classifiers. | Aidao Chen, Anindya De, Aravindan Vijayaraghavan |
| 2022 | ICASSP | Effective and Inconspicuous Over-the-Air Adversarial Examples with Adaptive Filtering. | Patrick O'Reilly, Pranjal Awasthi, Aravindan Vijayaraghavan, Bryan Pardo |
| 2021 | AISTATS | Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. | Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan |
| 2021 | ALT | Learning a mixture of two subspaces over finite fields. | Aidao Chen, Anindya De, Aravindan Vijayaraghavan |
| 2021 | COLT | Adversarially Robust Low Dimensional Representations. | Pranjal Awasthi, Vaggos Chatziafratis, Xue Chen, Aravindan Vijayaraghavan |
| 2021 | ICML | Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2020 | COLT | Estimating Principal Components under Adversarial Perturbations. | Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan |
| 2020 | FOCS | Scheduling Precedence-Constrained Jobs on Related Machines with Communication Delay. | Biswaroop Maiti, Rajmohan Rajaraman, David Stalfa, Zoya Svitkina, Aravindan Vijayaraghavan |
| 2019 | AISTATS | Block Stability for MAP Inference. | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2019 | FOCS | Smoothed Analysis in Unsupervised Learning via Decoupling. | Aditya Bhaskara, Aidao Chen, Aidan Perreault, Aravindan Vijayaraghavan |
| 2018 | AISTATS | Optimality of Approximate Inference Algorithms on Stable Instances. | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2018 | FOCS | Towards Learning Sparsely Used Dictionaries with Arbitrary Supports. | Pranjal Awasthi, Aravindan Vijayaraghavan |
| 2018 | ICML | Clustering Semi-Random Mixtures of Gaussians. | Pranjal Awasthi, Aravindan Vijayaraghavan |
| 2017 | FOCS | On Learning Mixtures of Well-Separated Gaussians. | Oded Regev, Aravindan Vijayaraghavan |
| 2017 | SODA | Approximation Algorithms for Label Cover and The Log-Density Threshold. | Eden Chlamtc, Pasin Manurangsi, Dana Moshkovitz, Aravindan Vijayaraghavan |
| 2016 | COLT | Learning Communities in the Presence of Errors. | Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan |
| 2015 | COLT | Correlation Clustering with Noisy Partial Information. | Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan |
| 2014 | COLT | Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold? | Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan |
| 2014 | COLT | Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability. | Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan |
| 2014 | SODA | Bilu-Linial Stable Instances of Max Cut and Minimum Multiway Cut. | Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan |
| 2014 | STOC | Smoothed analysis of tensor decompositions. | Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan |
| 2014 | STOC | Constant factor approximation for balanced cut in the PIE model. | Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan |
| 2012 | ICALP | On Quadratic Programming with a Ratio Objective. | Aditya Bhaskara, Moses Charikar, Rajsekar Manokaran, Aravindan Vijayaraghavan |
| 2012 | SODA | Polynomial integrality gaps for strong SDP relaxations of Densest | Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan, Venkatesan Guruswami, Yuan Zhou |
| 2012 | SODA | Approximation algorithms and hardness of the | Julia Chuzhoy, Yury Makarychev, Aravindan Vijayaraghavan, Yuan Zhou |
| 2012 | STOC | Approximation algorithms for semi-random partitioning problems. | Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan |
| 2011 | SODA | Approximating Matrix p-norms. | Aditya Bhaskara, Aravindan Vijayaraghavan |
| 2010 | STOC | Detecting high log-densities: an | Aditya Bhaskara, Moses Charikar, Eden Chlamtac, Uriel Feige, Aravindan Vijayaraghavan |