Vatsal Sharan

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 53

Top Topics

Convex Optimization

Range Aggregate Queries

Multiclass Learning

Top Venues

Publications

Haipeng Luo, Spandan Senapati, Vatsal Sharan
Optimal Multiclass U-Calibration Error and Beyond. CoRR (2024)
Puneesh Deora, Bhavya Vasudeva, Vatsal Sharan, Christos Thrampoulidis
Fast Test Error Rates for Gradient-Based Algorithms on Separable Data. ICASSP (2024)
Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
Open Problem: Can Local Regularization Learn All Multiclass Problems? COLT (2024)
Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan
Stability and Multigroup Fairness in Ranking with Uncertain Predictions. CoRR (2024)
Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
Learnability is a Compact Property. CoRR (2024)
Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
Regularization and Optimal Multiclass Learning. COLT (2024)
Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan
When is Multicalibration Post-Processing Necessary? CoRR (2024)
Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan
Simplicity Bias of Transformers to Learn Low Sensitivity Functions. CoRR (2024)
Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia
Pre-trained Large Language Models Use Fourier Features to Compute Addition. CoRR (2024)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). IJCAI (2023)
Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
Fairness in Matching under Uncertainty. CoRR (2023)
Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
Fairness in Matching under Uncertainty. ICML (2023)
Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. Proc. ACM Manag. Data 1 (1) (2023)
Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
Regularization and Optimal Multiclass Learning. CoRR (2023)
Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness. CoRR (2023)
Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan
Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models. CoRR (2023)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory. CoRR (2022)
Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder
Multicalibrated Partitions for Importance Weights. ALT (2022)
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder
Omnipredictors. ITCS (2022)
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
Efficient Convex Optimization Requires Superlinear Memory. COLT (2022)
Parikshit Gopalan, Nina Narodytska, Omer Reingold, Vatsal Sharan, Udi Wieder
KL Divergence Estimation with Multi-group Attribution. CoRR (2022)
Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. COLT (2022)
Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. CoRR (2022)
Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant
On the Statistical Complexity of Sample Amplification. CoRR (2022)
Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. CoRR (2021)
Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. ICLR (2021)
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder
Omnipredictors. CoRR (2021)
Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. CoRR (2021)
Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder
Multicalibrated Partitions for Importance Weights. CoRR (2021)
Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. ICML (2020)
Vatsal Sharan, Aaron Sidford, Gregory Valiant
Memory-sample tradeoffs for linear regression with small error. STOC (2019)
Vatsal Sharan, Aaron Sidford, Gregory Valiant
Memory-Sample Tradeoffs for Linear Regression with Small Error. CoRR (2019)
Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. CoRR (2019)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML (2019)
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
PIDForest: Anomaly Detection via Partial Identification. CoRR (2019)
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
PIDForest: Anomaly Detection via Partial Identification. NeurIPS (2019)
Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang
Recovery Guarantees For Quadratic Tensors With Sparse Observations. AISTATS (2019)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Prediction with a short memory. STOC (2018)
Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. Proc. VLDB Endow. 11 (11) (2018)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Sketching Linear Classifiers over Data Streams. SIGMOD Conference (2018)
Vatsal Sharan, Parikshit Gopalan, Udi Wieder
Efficient Anomaly Detection via Matrix Sketching. NeurIPS (2018)
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
A Spectral View of Adversarially Robust Features. CoRR (2018)
Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR (2018)
Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang
Recovery Guarantees for Quadratic Tensors with Limited Observations. CoRR (2018)
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Faster Anomaly Detection via Matrix Sketching. CoRR (2018)
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
A Spectral View of Adversarially Robust Features. NeurIPS (2018)
Vatsal Sharan, Gregory Valiant
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. ICML (2017)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Learning Overcomplete HMMs. NIPS (2017)
Vatsal Sharan, Gregory Valiant
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. CoRR (2017)
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
Finding Heavily-Weighted Features in Data Streams. CoRR (2017)
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
There and Back Again: A General Approach to Learning Sparse Models. CoRR (2017)
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
Learning Overcomplete HMMs. CoRR (2017)
Sham M. Kakade, Percy Liang, Vatsal Sharan, Gregory Valiant
Prediction with a Short Memory. CoRR (2016)
Vatsal Sharan, Rakesh Kumar Bansal
Large deviation property of waiting times for Markov and mixing processes. ISIT (2014)
Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde
Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks. ACSSC (2013)
Sudhir Kumar, Vatsal Sharan, Rajesh M. Hegde
Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks. GLOBECOM (2013)
Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde
Multiple source localization using randomly distributed wireless sensor nodes. COMSNETS (2013)