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2020 – today
- 2024
[c26]Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Regularization and Optimal Multiclass Learning. COLT 2024: 260-310- [c25]Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Open Problem: Can Local Regularization Learn All Multiclass Problems? COLT 2024: 5301-5305 - [c24]Puneesh Deora, Bhavya Vasudeva, Vatsal Sharan, Christos Thrampoulidis:
Fast Test Error Rates for Gradient-Based Algorithms on Separable Data. ICASSP 2024: 7440-7444 - [c23]Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan:
Stability and Multigroup Fairness in Ranking with Uncertain Predictions. ICML 2024 - [i30]Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan:
Stability and Multigroup Fairness in Ranking with Uncertain Predictions. CoRR abs/2402.09326 (2024) - [i29]Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Learnability is a Compact Property. CoRR abs/2402.10360 (2024) - [i28]Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan:
Simplicity Bias of Transformers to Learn Low Sensitivity Functions. CoRR abs/2403.06925 (2024) - [i27]Haipeng Luo, Spandan Senapati, Vatsal Sharan:
Optimal Multiclass U-Calibration Error and Beyond. CoRR abs/2405.19374 (2024) - [i26]Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia:
Pre-trained Large Language Models Use Fourier Features to Compute Addition. CoRR abs/2406.03445 (2024) - [i25]Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan:
When is Multicalibration Post-Processing Necessary? CoRR abs/2406.06487 (2024) - 2023
- [j2]Sepanta Zeighami
, Cyrus Shahabi, Vatsal Sharan:
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. Proc. ACM Manag. Data 1(1): 100:1-100:26 (2023) - [c22]Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova:
Fairness in Matching under Uncertainty. ICML 2023: 7775-7794 - [c21]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). IJCAI 2023: 6468-6473 - [i24]Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova:
Fairness in Matching under Uncertainty. CoRR abs/2302.03810 (2023) - [i23]Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Regularization and Optimal Multiclass Learning. CoRR abs/2309.13692 (2023) - [i22]Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan:
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness. CoRR abs/2310.06161 (2023) - [i21]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 abs/2310.17086 (2023) - 2022
- [c20]Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder:
Multicalibrated Partitions for Importance Weights. ALT 2022: 408-435 - [c19]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. COLT 2022: 2390-2430 - [c18]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: 2431-2540 - [c17]Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder:
Omnipredictors. ITCS 2022: 79:1-79:21 - [i20]Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant:
On the Statistical Complexity of Sample Amplification. CoRR abs/2201.04315 (2022) - [i19]Parikshit Gopalan, Nina Narodytska, Omer Reingold, Vatsal Sharan, Udi Wieder:
KL Divergence Estimation with Multi-group Attribution. CoRR abs/2202.13576 (2022) - [i18]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. CoRR abs/2203.15260 (2022) - [i17]Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan:
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. CoRR abs/2211.10832 (2022) - 2021
- [c16]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 - [i16]Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder:
Multicalibrated Partitions for Importance Weights. CoRR abs/2103.05853 (2021) - [i15]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 abs/2103.15261 (2021) - [i14]Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder:
Omnipredictors. CoRR abs/2109.05389 (2021) - [i13]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 abs/2111.03137 (2021) - 2020
- [c15]Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant:
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. ICML 2020: 442-451
2010 – 2019
- 2019
- [c14]Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang:
Recovery Guarantees For Quadratic Tensors With Sparse Observations. AISTATS 2019: 3322-3332 - [c13]Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML 2019: 5690-5700 - [c12]Parikshit Gopalan, Vatsal Sharan, Udi Wieder:
PIDForest: Anomaly Detection via Partial Identification. NeurIPS 2019: 15783-15793 - [c11]Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Memory-sample tradeoffs for linear regression with small error. STOC 2019: 890-901 - [i12]Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Memory-Sample Tradeoffs for Linear Regression with Small Error. CoRR abs/1904.08544 (2019) - [i11]Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant:
Sample Amplification: Increasing Dataset Size even when Learning is Impossible. CoRR abs/1904.12053 (2019) - [i10]Parikshit Gopalan, Vatsal Sharan, Udi Wieder:
PIDForest: Anomaly Detection via Partial Identification. CoRR abs/1912.03582 (2019) - 2018
- [j1]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): 1647-1660 (2018) - [c10]Vatsal Sharan, Parikshit Gopalan, Udi Wieder:
Efficient Anomaly Detection via Matrix Sketching. NeurIPS 2018: 8080-8091 - [c9]Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant:
A Spectral View of Adversarially Robust Features. NeurIPS 2018: 10159-10169 - [c8]Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant:
Sketching Linear Classifiers over Data Streams. SIGMOD Conference 2018: 757-772 - [c7]Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant:
Prediction with a short memory. STOC 2018: 1074-1087 - [i9]Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis:
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR abs/1803.01969 (2018) - [i8]Parikshit Gopalan, Vatsal Sharan, Udi Wieder:
Faster Anomaly Detection via Matrix Sketching. CoRR abs/1804.03065 (2018) - [i7]Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang:
Recovery Guarantees for Quadratic Tensors with Limited Observations. CoRR abs/1811.00148 (2018) - [i6]Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant:
A Spectral View of Adversarially Robust Features. CoRR abs/1811.06609 (2018) - 2017
- [c6]Vatsal Sharan, Gregory Valiant:
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. ICML 2017: 3095-3104 - [c5]Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant:
Learning Overcomplete HMMs. NIPS 2017: 940-949 - [i5]Vatsal Sharan, Gregory Valiant:
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. CoRR abs/1703.01804 (2017) - [i4]Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant:
There and Back Again: A General Approach to Learning Sparse Models. CoRR abs/1706.08146 (2017) - [i3]Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant:
Finding Heavily-Weighted Features in Data Streams. CoRR abs/1711.02305 (2017) - [i2]Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant:
Learning Overcomplete HMMs. CoRR abs/1711.02309 (2017) - 2016
- [i1]Sham M. Kakade, Percy Liang, Vatsal Sharan, Gregory Valiant:
Prediction with a Short Memory. CoRR abs/1612.02526 (2016) - 2014
- [c4]Vatsal Sharan, Rakesh Kumar Bansal:
Large deviation property of waiting times for Markov and mixing processes. ISIT 2014: 1096-1100 - 2013
- [c3]Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde:
Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks. ACSSC 2013: 542-546 - [c2]Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde:
Multiple source localization using randomly distributed wireless sensor nodes. COMSNETS 2013: 1-2 - [c1]Sudhir Kumar, Vatsal Sharan, Rajesh M. Hegde:
Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks. GLOBECOM 2013: 487-492
Coauthor Index
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