Gregory Valiant
35 papers · 2013–2023 · 7 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (10) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (7) π Cross-Pollinator (5)
π
Cross-Pollinator
(5)
π
Renaissance Researcher
(6)
πΊοΈ
Taxonomy Completionist
(50)
π±
Topic Pioneer
π
Keyword Champion
ποΈ
Keyword Collector
(179)
π
Century Club
(35)
π
Trend Setter
β
The Questioner
π₯
Unstoppable
(11)
β‘
Prolific Year
(6)
Conferences
NIPS (12)
ICML (11)
COLT (7)
AISTATS (2)
ACL (1)
IJCAI (1)
IJCNLP (1)
Top co-authors
Research topics
Keywords
convex optimization
(4)
statistical estimation
(4)
parameter estimation
(3)
sample complexity
(3)
gradient descent
(3)
first-order method
(2)
sublinear sample
(2)
earth mover distance
(2)
audio perception
(2)
image classification
(2)
distribution learning
(2)
linear regression
(2)
sublinear sampling
(2)
spectral method
(2)
feature learning
(2)
population estimation
(2)
in-context learning
(2)
learning theory
(2)
query complexity
(2)
polyperceivable audio
(2)
Papers
One-sided Matrix Completion from Two Observations Per Row
ICML 2023
Lexinvariant Language Models
NIPS 2023
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract)
IJCAI 2023
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
COLT 2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
NIPS 2022
Efficient Convex Optimization Requires Superlinear Memory
COLT 2022
Exponential Weights Algorithms for Selective Learning
COLT 2021
Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio
ACL 2021
Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio
IJCNLP 2021
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
ICML 2021
Misspecification in Prediction Problems and Robustness via Improper Learning
AISTATS 2021
On the Generalization Effects of Linear Transformations in Data Augmentation
ICML 2020
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process
COLT 2020
Worst-Case Analysis for Randomly Collected Data
NIPS 2020
Sublinear Optimal Policy Value Estimation in Contextual Bandits
AISTATS 2020
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
ICML 2020
A Theory of Selective Prediction
COLT 2019
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
NIPS 2019
Making AI Forget You: Data Deletion in Machine Learning
NIPS 2019
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
ICML 2019
Equivariant Transformer Networks
ICML 2019
Maximum Likelihood Estimation for Learning Populations of Parameters
ICML 2019
A Spectral View of Adversarially Robust Features
NIPS 2018
Estimating Learnability in the Sublinear Data Regime
NIPS 2018
A Data Prism: Semi-verified learning in the small-alpha regime
COLT 2018
Learning Populations of Parameters
NIPS 2017
Learning Overcomplete HMMs
NIPS 2017
Estimating the unseen from multiple populations
ICML 2017
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
ICML 2017
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
NIPS 2016
Memory, Communication, and Statistical Queries
COLT 2016
Testing Closeness With Unequal Sized Samples
NIPS 2015
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
ICML 2014
Learning Polynomials with Neural Networks
ICML 2014
Estimating the Unseen: Improved Estimators for Entropy and other Properties
NIPS 2013