Yair Carmon
27 papers · 2017–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (8) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (5) π Cross-Pollinator (8)
π
Conference Polyglot
(5)
π
Academic Marathon
(8)
π
Renaissance Researcher
(5)
π₯
Mega-Team
(60)
π
Triple Crown
π¬
Deep Specialist
(13)
β‘
Prolific Year
(6)
π
Trend Setter
π
Century Club
(27)
ποΈ
Keyword Collector
(98)
π₯
Unstoppable
(9)
Conferences
NIPS (12)
COLT (6)
ICML (6)
ICLR (2)
EMNLP (1)
Top co-authors
Keywords
convex optimization
(7)
stochastic convex optimization
(4)
gradient descent
(4)
accelerated gradient
(3)
oracle complexity
(3)
parameter-free optimization
(3)
neural network optimization
(3)
data filtering
(2)
second-order method
(2)
multilevel monte carlo
(2)
stochastic gradient descent
(2)
distributionally robust optimization
(2)
stochastic method
(2)
lipschitz function
(2)
loss landscape
(2)
language model
(2)
non-convex optimization
(2)
stochastic optimization
(2)
convex loss
(2)
scaling law
(2)
Papers
Language models scale reliably with over-training and on downstream tasks
ICLR 2025
The Price of Adaptivity in Stochastic Convex Optimization
COLT 2024
Resolving Discrepancies in Compute-Optimal Scaling of Language Models
NIPS 2024
DataComp-LM: In search of the next generation of training sets for language models
NIPS 2024
Accelerated Parameter-Free Stochastic Optimization
COLT 2024
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds
ICLR 2023
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond
ICML 2023
DoG is SGDβs Best Friend: A Parameter-Free Dynamic Step Size Schedule
ICML 2023
DataComp: In search of the next generation of multimodal datasets
NIPS 2023
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments
EMNLP 2022
Optimal and Adaptive Monteiro-Svaiter Acceleration
NIPS 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
ICML 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
ICML 2022
Making SGD Parameter-Free
COLT 2022
Distributionally Robust Optimization via Ball Oracle Acceleration
NIPS 2022
Never Go Full Batch (in Stochastic Convex Optimization)
NIPS 2021
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
ICML 2021
Stochastic Bias-Reduced Gradient Methods
NIPS 2021
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
COLT 2021
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
COLT 2020
Large-Scale Methods for Distributionally Robust Optimization
NIPS 2020
Acceleration with a Ball Optimization Oracle
NIPS 2020
A Rank-1 Sketch for Matrix Multiplicative Weights
COLT 2019
Variance Reduction for Matrix Games
NIPS 2019
Unlabeled Data Improves Adversarial Robustness
NIPS 2019
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
NIPS 2018
βConvex Until Proven Guiltyβ: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions
ICML 2017