Vatsal Sharan - RankMe

89 papers A* 23A 4B 4Misc 3Journal 53Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Unified Approach to Memory-Sample Tradeoffs for Detecting Planted Structures. CoRR Sumegha Garg, Jabari Hastings, Chirag Pabbaraju, Vatsal Sharan
2026	A*	`conf`	An External Fairness Evaluation of LinkedIn Talent Search. AAAI Tina Behzad, Siddartha Devic, Vatsal Sharan, Aleksandra Korolova, David Kempe
2026	A	`conf`	Auditability and the Landscape of Distance to Multicalibration. ITCS Nathan Derhake, Siddartha Devic, Dutch Hansen, Kuan Liu, Vatsal Sharan
2026	J	`jnl`	Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge. CoRR Bhavya Vasudeva, Puneesh Deora, Alberto Bietti, Vatsal Sharan, Christos Thrampoulidis
2025	J	`jnl`	An External Fairness Evaluation of LinkedIn Talent Search. CoRR Tina Behzad, Siddartha Devic, Vatsal Sharan, Aleksandra Korolova, David Kempe
2025	J	`jnl`	Auditability and the Landscape of Distance to Multicalibration. CoRR Nathan Derhake, Siddartha Devic, Dutch Hansen, Kuan Liu, Vatsal Sharan
2025	J	`jnl`	Efficient Swap Multicalibration of Elicitable Properties. CoRR Lunjia Hu, Haipeng Luo, Spandan Senapati, Vatsal Sharan
2025	J	`jnl`	FoNE: Precise Single-Token Number Embeddings via Fourier Features. CoRR Tianyi Zhou, Deqing Fu, Mahdi Soltanolkotabi, Robin Jia, Vatsal Sharan
2025	J	`jnl`	From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered. CoRR Siddartha Devic, Tejas Srinivasan, Jesse Thomason, Willie Neiswanger, Vatsal Sharan
2025	J	`jnl`	How Muon's Spectral Design Benefits Generalization: A Study on Imbalanced Data. CoRR Bhavya Vasudeva, Puneesh Deora, Yize Zhao, Vatsal Sharan, Christos Thrampoulidis
2025	J	`jnl`	Improved Bounds for Swap Multicalibration and Swap Omniprediction. CoRR Haipeng Luo, Spandan Senapati, Vatsal Sharan
2025	J	`jnl`	Latent Concept Disentanglement in Transformer-based Language Models. CoRR Guanzhe Hong, Bhavya Vasudeva, Vatsal Sharan, Cyrus Rashtchian, Prabhakar Raghavan, Rina Panigrahy
2025	J	`jnl`	Limitations on Safe, Trusted, Artificial General Intelligence. CoRR Rina Panigrahy, Vatsal Sharan
2025	J	`jnl`	On the Inherent Privacy of Zeroth Order Projected Gradient Descent. CoRR Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan
2025	A	`conf`	On the Inherent Privacy of Zeroth-Order Projected Gradient Descent. AISTATS Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan
2025	B	`conf`	Proper Learnability and the Role of Unlabeled Data. ALT Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2025	J	`jnl`	Proper Learnability and the Role of Unlabeled Data. CoRR Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2025	J	`jnl`	Simultaneous Swap Regret Minimization via KL-Calibration. CoRR Haipeng Luo, Spandan Senapati, Vatsal Sharan
2025	J	`jnl`	Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models. CoRR Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Dani Yogatama, Vatsal Sharan, Robin Jia, Willie Neiswanger
2025	J	`jnl`	The Rich and the Simple: On the Implicit Bias of Adam and SGD. CoRR Bhavya Vasudeva, Jung Whan Lee, Vatsal Sharan, Mahdi Soltanolkotabi
2025	A*	`conf`	Transformers Learn Low Sensitivity Functions: Investigations and Implications. ICLR Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan
2025	J	`jnl`	When Do Transformers Learn Heuristics for Graph Connectivity? CoRR Qilin Ye, Deqing Fu, Robin Jia, Vatsal Sharan
2025	J	`jnl`	Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning. CoRR Ang Li, Charles Wang, Kaiyu Yue, Zikui Cai, Ollie Liu, Deqing Fu, Peng Guo, Wang Bill Zhu, Vatsal Sharan, Robin Jia, Willie Neiswanger, Furong Huang, Tom Goldstein, Micah Goldblum
2024	J	`jnl`	Discovering Data Structures: Nearest Neighbor Search and Beyond. CoRR Omar Salemohamed, Laurent Charlin, Shivam Garg, Vatsal Sharan, Gregory Valiant
2024	J	`jnl`	Efficient Convex Optimization Requires Superlinear Memory. J. ACM Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
2024	Misc	`conf`	Fast Test Error Rates for Gradient-Based Algorithms on Separable Data. ICASSP Puneesh Deora, Bhavya Vasudeva, Vatsal Sharan, Christos Thrampoulidis
2024	J	`jnl`	Learnability is a Compact Property. CoRR Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2024	J	`jnl`	Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness. Trans. Mach. Learn. Res. Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
2024	A*	`conf`	Open Problem: Can Local Regularization Learn All Multiclass Problems? COLT Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2024	A*	`conf`	Optimal Multiclass U-Calibration Error and Beyond. NeurIPS Haipeng Luo, Spandan Senapati, Vatsal Sharan
2024	J	`jnl`	Optimal Multiclass U-Calibration Error and Beyond. CoRR Haipeng Luo, Spandan Senapati, Vatsal Sharan
2024	A*	`conf`	Pre-trained Large Language Models Use Fourier Features to Compute Addition. NeurIPS Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia
2024	J	`jnl`	Pre-trained Large Language Models Use Fourier Features to Compute Addition. CoRR Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia
2024	A*	`conf`	Regularization and Optimal Multiclass Learning. COLT Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2024	J	`jnl`	Simplicity Bias of Transformers to Learn Low Sensitivity Functions. CoRR Bhavya Vasudeva, Deqing Fu, Tianyi Zhou, Elliott Kau, Youqi Huang, Vatsal Sharan
2024	A*	`conf`	Stability and Multigroup Fairness in Ranking with Uncertain Predictions. ICML Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan
2024	J	`jnl`	Stability and Multigroup Fairness in Ranking with Uncertain Predictions. CoRR Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan
2024	A*	`conf`	Transductive Learning is Compact. NeurIPS Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2024	A*	`conf`	Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression. NeurIPS Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan
2024	A*	`conf`	When is Multicalibration Post-Processing Necessary? NeurIPS Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan
2024	J	`jnl`	When is Multicalibration Post-Processing Necessary? CoRR Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan
2023	A*	`conf`	Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). IJCAI Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
2023	A*	`conf`	Fairness in Matching under Uncertainty. ICML Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
2023	J	`jnl`	Fairness in Matching under Uncertainty. CoRR Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
2023	J	`jnl`	Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness. CoRR Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
2023	J	`jnl`	NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. Proc. ACM Manag. Data Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan
2023	J	`jnl`	Regularization and Optimal Multiclass Learning. CoRR Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
2023	J	`jnl`	Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models. CoRR Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan
2022	A*	`conf`	Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. COLT Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
2022	A*	`conf`	Efficient Convex Optimization Requires Superlinear Memory. COLT Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
2022	J	`jnl`	Efficient Convex Optimization Requires Superlinear Memory. CoRR Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
2022	J	`jnl`	KL Divergence Estimation with Multi-group Attribution. CoRR Parikshit Gopalan, Nina Narodytska, Omer Reingold, Vatsal Sharan, Udi Wieder
2022	B	`conf`	Multicalibrated Partitions for Importance Weights. ALT Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder
2022	J	`jnl`	NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks. CoRR Sepanta Zeighami, Cyrus Shahabi, Vatsal Sharan
2022	A	`conf`	Omnipredictors. ITCS Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder
2022	J	`jnl`	On the Statistical Complexity of Sample Amplification. CoRR Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant
2021	J	`jnl`	Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. CoRR Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
2021	J	`jnl`	Multicalibrated Partitions for Importance Weights. CoRR Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder
2021	J	`jnl`	Omnipredictors. CoRR Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder
2021	A*	`conf`	One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. ICLR Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
2021	J	`jnl`	One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. CoRR Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
2020	A*	`conf`	Sample Amplification: Increasing Dataset Size even when Learning is Impossible. ICML Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
2019	A*	`conf`	Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. ICML Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
2019	J	`jnl`	Memory-Sample Tradeoffs for Linear Regression with Small Error. CoRR Vatsal Sharan, Aaron Sidford, Gregory Valiant
2019	A*	`conf`	Memory-sample tradeoffs for linear regression with small error. STOC Vatsal Sharan, Aaron Sidford, Gregory Valiant
2019	A*	`conf`	PIDForest: Anomaly Detection via Partial Identification. NeurIPS Parikshit Gopalan, Vatsal Sharan, Udi Wieder
2019	J	`jnl`	PIDForest: Anomaly Detection via Partial Identification. CoRR Parikshit Gopalan, Vatsal Sharan, Udi Wieder
2019	A	`conf`	Recovery Guarantees For Quadratic Tensors With Sparse Observations. AISTATS Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang
2019	J	`jnl`	Sample Amplification: Increasing Dataset Size even when Learning is Impossible. CoRR Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
2018	A*	`conf`	A Spectral View of Adversarially Robust Features. NeurIPS Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
2018	J	`jnl`	A Spectral View of Adversarially Robust Features. CoRR Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant
2018	A*	`conf`	Efficient Anomaly Detection via Matrix Sketching. NeurIPS Vatsal Sharan, Parikshit Gopalan, Udi Wieder
2018	J	`jnl`	Faster Anomaly Detection via Matrix Sketching. CoRR Parikshit Gopalan, Vatsal Sharan, Udi Wieder
2018	J	`jnl`	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. CoRR Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
2018	J	`jnl`	Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. Proc. VLDB Endow. Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
2018	A*	`conf`	Prediction with a short memory. STOC Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
2018	J	`jnl`	Recovery Guarantees for Quadratic Tensors with Limited Observations. CoRR Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang
2018	—	`conf`	Sketching Linear Classifiers over Data Streams. SIGMOD Conference Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
2017	J	`jnl`	Finding Heavily-Weighted Features in Data Streams. CoRR Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
2017	—	`conf`	Learning Overcomplete HMMs. NIPS Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
2017	J	`jnl`	Learning Overcomplete HMMs. CoRR Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant
2017	A*	`conf`	Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. ICML Vatsal Sharan, Gregory Valiant
2017	J	`jnl`	Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use. CoRR Vatsal Sharan, Gregory Valiant
2017	J	`jnl`	There and Back Again: A General Approach to Learning Sparse Models. CoRR Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
2016	J	`jnl`	Prediction with a Short Memory. CoRR Sham M. Kakade, Percy Liang, Vatsal Sharan, Gregory Valiant
2014	B	`conf`	Large deviation property of waiting times for Markov and mixing processes. ISIT Vatsal Sharan, Rakesh Kumar Bansal
2013	B	`conf`	Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks. GLOBECOM Sudhir Kumar, Vatsal Sharan, Rajesh M. Hegde
2013	Misc	`conf`	Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks. ACSSC Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde
2013	Misc	`conf`	Multiple source localization using randomly distributed wireless sensor nodes. COMSNETS Vatsal Sharan, Sudhir Kumar, Rajesh M. Hegde