Suriya Gunasekar
24 papers · 2014–2024 · 8 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (10) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird
🏃
Academic Marathon
(10)
🧭
Keyword Pioneer
🐣
Hot Topic Early Bird
🗃️
Keyword Collector
(89)
💎
Century Club
(24)
🔥
Unstoppable
(11)
📈
Trend Setter
🚀
Conference Pioneer
Conferences
NIPS (8)
ICML (4)
AISTATS (3)
COLT (3)
ICLR (3)
ECCV (1)
JMLR (1)
MLHC (1)
Top co-authors
Keywords
gradient descent
(9)
implicit bia
(5)
matrix completion
(4)
implicit regularization
(4)
representation learning
(2)
learning theory
(2)
equalized odd
(2)
algorithmic fairness
(2)
convergence rate
(2)
gradient flow
(2)
low-rank matrix
(2)
separable datum
(2)
kernel regime
(2)
convex optimization
(2)
natural gradient
(2)
mirror descent
(2)
deep learning
(1)
sample complexity
(1)
online learning
(1)
data augmentation
(1)
Papers
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
ICLR 2024
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
ICLR 2024
How to Fine-Tune Vision Models with SGD
ICLR 2024
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
NIPS 2023
Neural-Sim: Learning to Generate Training Data with NeRF
ECCV 2022
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
COLT 2022
Data Augmentation as Feature Manipulation
ICML 2022
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent
AISTATS 2021
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
NIPS 2020
Implicit Regularization and Convergence for Weight Normalization
NIPS 2020
Kernel and Rich Regimes in Overparametrized Models
COLT 2020
Convergence of Gradient Descent on Separable Data
AISTATS 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
ICML 2019
Characterizing Implicit Bias in Terms of Optimization Geometry
ICML 2018
The Implicit Bias of Gradient Descent on Separable Data
JMLR 2018
On preserving non-discrimination when combining expert advice
NIPS 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks
NIPS 2018
Learning Non-Discriminatory Predictors
COLT 2017
Implicit Regularization in Matrix Factorization
NIPS 2017
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization
MLHC 2016
Preference Completion from Partial Rankings
NIPS 2016
Consistent Collective Matrix Completion under Joint Low Rank Structure
AISTATS 2015
Unified View of Matrix Completion under General Structural Constraints
NIPS 2015
Exponential Family Matrix Completion under Structural Constraints
ICML 2014