Vatsal Sharan
27 papers · 2017–2026 · 9 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (8) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (7) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (9)
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Hot Topic Early Bird
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Conference Polyglot
(7)
🏃
Academic Marathon
(8)
🏆
Keyword Champion
(2)
🏆
Grand Slam
🔥
Unstoppable
(9)
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Prolific Year
(8)
💎
Century Club
(25)
❓
The Questioner
(3)
📈
Trend Setter
🗃️
Keyword Collector
(108)
Conferences
NIPS (9)
ICML (5)
COLT (4)
AISTATS (2)
ALT (2)
ICLR (2)
AAAI (1)
ACL (1)
IJCAI (1)
Top co-authors
Keywords
convex optimization
(3)
learning theory
(2)
empirical risk minimization
(2)
multiclass classification
(2)
spectral method
(2)
transductive learning
(2)
anomaly detection
(2)
query complexity
(2)
calibration error
(2)
algorithmic fairness
(2)
non-convex optimization
(1)
density estimation
(1)
parameter estimation
(1)
mathematical reasoning
(1)
online learning
(1)
feature learning
(1)
compressive sensing
(1)
pac learning
(1)
principal component analysis
(1)
sample complexity
(1)
Papers
An External Fairness Evaluation of LinkedIn Talent Search
AAAI 2026
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
ACL 2026
Transformers Learn Low Sensitivity Functions: Investigations and Implications
ICLR 2025
On the Inherent Privacy of Zeroth-Order Projected Gradient Descent
AISTATS 2025
Proper Learnability and the Role of Unlabeled Data
ALT 2025
Regularization and Optimal Multiclass Learning
COLT 2024
Transductive Learning is Compact
NIPS 2024
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
ICML 2024
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
NIPS 2024
Open Problem: Can Local Regularization Learn All Multiclass Problems?
COLT 2024
Optimal Multiclass U-Calibration Error and Beyond
NIPS 2024
Pre-trained Large Language Models Use Fourier Features to Compute Addition
NIPS 2024
When is Multicalibration Post-Processing Necessary?
NIPS 2024
Fairness in Matching under Uncertainty
ICML 2023
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract)
IJCAI 2023
Efficient Convex Optimization Requires Superlinear Memory
COLT 2022
Multicalibrated Partitions for Importance Weights
ALT 2022
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
COLT 2022
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
ICLR 2021
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
ICML 2020
Recovery Guarantees For Quadratic Tensors With Sparse Observations
AISTATS 2019
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
ICML 2019
PIDForest: Anomaly Detection via Partial Identification
NIPS 2019
A Spectral View of Adversarially Robust Features
NIPS 2018
Efficient Anomaly Detection via Matrix Sketching
NIPS 2018
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
ICML 2017
Learning Overcomplete HMMs
NIPS 2017