Klivans, Adam

39 publications

NeurIPS 2025 Ambient Diffusion Omni: Training Good Models with Bad Data Giannis Daras, Adrian Rodriguez-Munoz, Adam Klivans, Antonio Torralba, Constantinos Costis Daskalakis

NeurIPS 2025 Ambient Proteins - Training Diffusion Models on Noisy Structures Giannis Daras, Jeffrey Ouyang-Zhang, Krithika Ravishankar, Constantinos Costis Daskalakis, Adam Klivans, Daniel Jesus Diaz

ICLR 2025 Distilling Structural Representations into Protein Sequence Models Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Kraehenbuehl, Adam Klivans, Daniel Jesus Diaz

ICML 2025 Does Generation Require Memorization? Creative Diffusion Models Using Ambient Diffusion Kulin Shah, Alkis Kalavasis, Adam Klivans, Giannis Daras

COLT 2025 Learning Constant-Depth Circuits in Malicious Noise Models Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

NeurIPS 2025 Learning Juntas Under Markov Random Fields Gautam Chandrasekaran, Adam Klivans

ICLR 2025 Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees Gautam Chandrasekaran, Adam Klivans, Lin Lin Lee, Konstantinos Stavropoulos

NeurIPS 2025 The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination Adam Klivans, Konstantinos Stavropoulos, Kevin Tian, Arsen Vasilyan

ICLR 2024 An Efficient Tester-Learner for Halfspaces Aravind Gollakota, Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

NeurIPSW 2024 Distilling Structural Representations into Protein Sequence Models Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Kraehenbuehl, Adam Klivans, Daniel Jesus Diaz

ICML 2024 Evolution-Inspired Loss Functions for Protein Representation Learning Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz

ICLRW 2024 Evolution-Inspired Loss Functions for Protein Representation Learning Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz

COLT 2024 Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

COLT 2024 Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension Gautam Chandrasekaran, Adam Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos

COLT 2024 Testable Learning with Distribution Shift Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

NeurIPS 2023 Agnostically Learning Single-Index Models Using Omnipredictors Aravind Gollakota, Parikshit Gopalan, Adam Klivans, Konstantinos Stavropoulos

NeurIPS 2023 Ambient Diffusion: Learning Clean Distributions from Corrupted Data Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam Klivans

NeurIPSW 2023 Binding Oracle: Fine-Tuning from Stability to Binding Free Energy Chengyue Gong, Adam Klivans, Jordan Wells, James Loy, Qiang Liu, Alex Dimakis, Daniel Diaz

ICLR 2023 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

NeurIPS 2023 Learning Mixtures of Gaussians Using the DDPM Objective Kulin Shah, Sitan Chen, Adam Klivans

COLT 2023 Learning Narrow One-Hidden-Layer ReLU Networks Sitan Chen, Zehao Dou, Surbhi Goel, Adam Klivans, Raghu Meka

NeurIPSW 2023 Microenvironment Flows as Protein Engineers Chengyue Gong, Lemeng Wu, Daniel Diaz, Xingchao Liu, James Loy, Adam Klivans, Qiang Liu

NeurIPS 2023 Predicting a Protein's Stability Under a Million Mutations Jeffrey Ouyang-Zhang, Daniel Diaz, Adam Klivans, Philipp Kraehenbuehl

NeurIPS 2023 Tester-Learners for Halfspaces: Universal Algorithms Aravind Gollakota, Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

NeurIPS 2022 Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks Sitan Chen, Aravind Gollakota, Adam Klivans, Raghu Meka

NeurIPSW 2022 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

NeurIPS 2021 Efficiently Learning One Hidden Layer ReLU Networks from Queries Sitan Chen, Adam Klivans, Raghu Meka

NeurIPS 2020 From Boltzmann Machines to Neural Networks and Back Again Surbhi Goel, Adam Klivans, Frederic Koehler

ICML 2020 Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

NeurIPS 2020 Statistical-Query Lower Bounds via Functional Gradients Surbhi Goel, Aravind Gollakota, Adam Klivans

ICML 2020 Superpolynomial Lower Bounds for Learning One-Layer Neural Networks Using Gradient Descent Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans

NeurIPS 2019 List-Decodable Linear Regression Sushrut Karmalkar, Adam Klivans, Pravesh Kothari

NeurIPS 2019 Time/Accuracy Tradeoffs for Learning a ReLU with Respect to Gaussian Marginals Surbhi Goel, Sushrut Karmalkar, Adam Klivans

ICLR 2018 Hyperparameter Optimization: A Spectral Approach Elad Hazan, Adam Klivans, Yang Yuan

ICML 2018 Learning One Convolutional Layer with Overlapping Patches Surbhi Goel, Adam Klivans, Raghu Meka

NeurIPS 2017 Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks Surbhi Goel, Adam Klivans

ICML 2017 Exact MAP Inference by Avoiding Fractional Vertices Erik M. Lindgren, Alexandros G. Dimakis, Adam Klivans

COLT 2017 Reliably Learning the ReLU in Polynomial Time Surbhi Goel, Varun Kanade, Adam Klivans, Justin Thaler

NeurIPS 2014 Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis, Adam Klivans