← Authors

Adam Klivans

32 papers · 2013–2025 · 4 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (44) πŸ”¬ Deep Specialist (17) πŸ† Keyword Champion (4) ⚑ Prolific Year (7) πŸš€ Conference Pioneer ❓ The Questioner πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (116) πŸ’Ž Century Club (32) πŸ”₯ Unstoppable (9)

Conferences

NIPS (13) COLT (8) ICML (6) ICLR (5)

Top co-authors

Surbhi Goel (8) Konstantinos Stavropoulos (8) Raghu Meka (7) Aravind Gollakota (7) Arsen Vasilyan (5) Sitan Chen (4) Chengyue Gong (4) Kulin Shah (3) Sushrut Karmalkar (3) Daniel Jesus Diaz (3)

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