Adam Klivans
32 papers · 2013–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (4) π Academic Marathon (12) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (6)
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Cross-Pollinator
(6)
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Renaissance Researcher
(6)
πΊοΈ
Taxonomy Completionist
(44)
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Deep Specialist
(17)
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Keyword Champion
(4)
β‘
Prolific Year
(7)
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Conference Pioneer
β
The Questioner
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Trend Setter
ποΈ
Keyword Collector
(116)
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Century Club
(32)
π₯
Unstoppable
(9)
Conferences
NIPS (13)
COLT (8)
ICML (6)
ICLR (5)
Top co-authors
Keywords
neural network
(8)
learning theory
(7)
relu network
(4)
agnostic learning
(4)
relu activation
(4)
pac learning
(3)
testable learning
(3)
gradient descent
(3)
graphical model
(2)
rectified linear unit
(2)
lower bound
(2)
log-concave distribution
(2)
distribution shift
(2)
adversarial corruption
(2)
diffusion model
(2)
statistical query
(2)
theoretical analysis
(1)
neural network optimization
(1)
domain adaptation
(1)
deep learning
(1)
Papers
Distilling Structural Representations into Protein Sequence Models
ICLR 2025
Learning Constant-Depth Circuits in Malicious Noise Models
COLT 2025
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
ICLR 2025
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
ICML 2025
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds
COLT 2024
Testable Learning with Distribution Shift
COLT 2024
An Efficient Tester-Learner for Halfspaces
ICLR 2024
Evolution-Inspired Loss Functions for Protein Representation Learning
ICML 2024
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension
COLT 2024
Learning Narrow One-Hidden-Layer ReLU Networks
COLT 2023
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
NIPS 2023
Tester-Learners for Halfspaces: Universal Algorithms
NIPS 2023
Agnostically Learning Single-Index Models using Omnipredictors
NIPS 2023
Learning Mixtures of Gaussians Using the DDPM Objective
NIPS 2023
Predicting a Protein's Stability under a Million Mutations
NIPS 2023
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
ICLR 2023
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
NIPS 2022
Efficiently Learning One Hidden Layer ReLU Networks From Queries
NIPS 2021
From Boltzmann Machines to Neural Networks and Back Again
NIPS 2020
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
ICML 2020
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
ICML 2020
Statistical-Query Lower Bounds via Functional Gradients
NIPS 2020
List-decodable Linear Regression
NIPS 2019
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
NIPS 2019
Efficient Algorithms for Outlier-Robust Regression
COLT 2018
Hyperparameter optimization: a spectral approach
ICLR 2018
Learning One Convolutional Layer with Overlapping Patches
ICML 2018
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
NIPS 2017
Reliably Learning the ReLU in Polynomial Time
COLT 2017
Exact MAP Inference by Avoiding Fractional Vertices
ICML 2017
Sparse Polynomial Learning and Graph Sketching
NIPS 2014
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching
COLT 2013