Vatsal Sharan

According to our database, Vatsal Sharan authored at least 41 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

When is Multicalibration Post-Processing Necessary?

Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan

CoRR, 2024

Pre-trained Large Language Models Use Fourier Features to Compute Addition.

Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia

CoRR, 2024

Optimal Multiclass U-Calibration Error and Beyond.

Haipeng Luo, Spandan Senapati, Vatsal Sharan

CoRR, 2024

Simplicity Bias of Transformers to Learn Low Sensitivity Functions.

Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan

CoRR, 2024

Learnability is a Compact Property.

Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng

CoRR, 2024

Stability and Multigroup Fairness in Ranking with Uncertain Predictions.

Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fast Test Error Rates for Gradient-Based Algorithms on Separable Data.

Puneesh Deora, Bhavya Vasudeva, Vatsal Sharan, Christos Thrampoulidis

Proceedings of the IEEE International Conference on Acoustics, 2024

Open Problem: Can Local Regularization Learn All Multiclass Problems?

Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Regularization and Optimal Multiclass Learning.

Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks.

Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan

Proc. ACM Manag. Data, 2023

Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models.

Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan

CoRR, 2023

Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness.

Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan

CoRR, 2023

Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract).

Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Fairness in Matching under Uncertainty.

Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova

Proceedings of the International Conference on Machine Learning, 2023

2022

KL Divergence Estimation with Multi-group Attribution.

Parikshit Gopalan, Nina Narodytska, Omer Reingold, Vatsal Sharan, Udi Wieder

CoRR, 2022

On the Statistical Complexity of Sample Amplification.

Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant

CoRR, 2022

Omnipredictors.

Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder

Proceedings of the 13th Innovations in Theoretical Computer Science Conference, 2022

Efficient Convex Optimization Requires Superlinear Memory.

Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales.

Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Multicalibrated Partitions for Importance Weights.

Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks.

Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Sample Amplification: Increasing Dataset Size even when Learning is Impossible.

Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Memory-sample tradeoffs for linear regression with small error.

Vatsal Sharan, Aaron Sidford, Gregory Valiant

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

PIDForest: Anomaly Detection via Partial Identification.

Parikshit Gopalan, Vatsal Sharan, Udi Wieder

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data.

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

Proceedings of the 36th International Conference on Machine Learning, 2019

Recovery Guarantees For Quadratic Tensors With Sparse Observations.

Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries.

Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis

Proc. VLDB Endow., 2018

Recovery Guarantees for Quadratic Tensors with Limited Observations.

Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang

CoRR, 2018

Faster Anomaly Detection via Matrix Sketching.

Parikshit Gopalan, Vatsal Sharan, Udi Wieder

CoRR, 2018

Prediction with a short memory.

Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant

Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Sketching Linear Classifiers over Data Streams.

Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

Proceedings of the 2018 International Conference on Management of Data, 2018

Efficient Anomaly Detection via Matrix Sketching.

Vatsal Sharan, Parikshit Gopalan, Udi Wieder

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Spectral View of Adversarially Robust Features.

Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Finding Heavily-Weighted Features in Data Streams.

Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

CoRR, 2017

There and Back Again: A General Approach to Learning Sparse Models.

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

CoRR, 2017

Learning Overcomplete HMMs.

Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use.

Vatsal Sharan, Gregory Valiant

Proceedings of the 34th International Conference on Machine Learning, 2017

2014

Large deviation property of waiting times for Markov and mixing processes.

Vatsal Sharan, Rakesh Kumar Bansal

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

2013

Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks.

Sudhir Kumar, Vatsal Sharan, Rajesh M. Hegde

Proceedings of the 2013 IEEE Global Communications Conference, 2013

Multiple source localization using randomly distributed wireless sensor nodes.

Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde

Proceedings of the Fifth International Conference on Communication Systems and Networks, 2013

Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks.

Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde

Proceedings of the 2013 Asilomar Conference on Signals, 2013