Adam R. Klivans

166 papers A* 63A 7B 1Journal 84Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Ambient Dataloops: Generative Models for Dataset Refinement. CoRR Adrián Rodríguez-Muñoz, William Daspit, Adam R. Klivans, Antonio Torralba, Constantinos Daskalakis, Giannis Daras
2026	J	`jnl`	Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension. CoRR Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2025	J	`jnl`	A Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube. CoRR Gautam Chandrasekaran, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2025	J	`jnl`	Ambient Diffusion Omni: Training Good Models with Bad Data. CoRR Giannis Daras, Adrián Rodríguez-Muñoz, Adam R. Klivans, Antonio Torralba, Constantinos Daskalakis
2025	A*	`conf`	Distilling Structural Representations into Protein Sequence Models. ICLR Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Krähenbühl, Adam R. Klivans, Daniel Jesus Diaz
2025	A*	`conf`	Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. ICML Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras
2025	J	`jnl`	Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. CoRR Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras
2025	A*	`conf`	Learning Constant-Depth Circuits in Malicious Noise Models. COLT Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2025	J	`jnl`	Learning Juntas under Markov Random Fields. CoRR Gautam Chandrasekaran, Adam R. Klivans
2025	A*	`conf`	Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees. ICLR Gautam Chandrasekaran, Adam R. Klivans, Lin Lin Lee, Konstantinos Stavropoulos
2025	J	`jnl`	Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees. CoRR Gautam Chandrasekaran, Adam R. Klivans, Lin Lin Lee, Konstantinos Stavropoulos
2025	A*	`conf`	Learning the Sherrington-Kirkpatrick Model Even at Low Temperature. STOC Gautam Chandrasekaran, Adam R. Klivans
2025	J	`jnl`	Testing Noise Assumptions of Learning Algorithms. CoRR Surbhi Goel, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2025	J	`jnl`	The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination. CoRR Adam R. Klivans, Konstantinos Stavropoulos, Kevin Tian, Arsen Vasilyan
2025	J	`jnl`	Triangle Multiplication Is All You Need For Biomolecular Structure Representations. CoRR Jeffrey Ouyang-Zhang, Pranav Murugan, Daniel Jesus Diaz, Gianluca Scarpellini, Richard Strong Bowen, Nate Gruver, Adam R. Klivans, Philipp Krähenbühl, Aleksandra Faust, Maruan Al-Shedivat
2024	A*	`conf`	An Efficient Tester-Learner for Halfspaces. ICLR Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2024	A*	`conf`	Efficient Discrepancy Testing for Learning with Distribution Shift. NeurIPS Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan
2024	J	`jnl`	Efficient Discrepancy Testing for Learning with Distribution Shift. CoRR Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan
2024	A*	`conf`	Evolution-Inspired Loss Functions for Protein Representation Learning. ICML Chengyue Gong, Adam R. Klivans, James Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz
2024	J	`jnl`	ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. Hyunsu Chae, Keren Zhu, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Daniel De Araujo, Jay Reddy, Adam R. Klivans, David Z. Pan
2024	B	`conf`	ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning. ASPDAC Hyunsu Chae, Keren Zhu, Bhyrav Mutnury, Zixuan Jiang, Daniel De Araujo, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, David Z. Pan
2024	J	`jnl`	Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world. AI Mag. Adam R. Klivans, Alexandros G. Dimakis, Kristen Grauman, Jonathan I. Tamir, Daniel Jesus Diaz, Karen Davidson
2024	J	`jnl`	Learning Constant-Depth Circuits in Malicious Noise Models. CoRR Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2024	A*	`conf`	Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. COLT Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2024	J	`jnl`	Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. CoRR Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2024	J	`jnl`	Learning the Sherrington-Kirkpatrick Model Even at Low Temperature. CoRR Gautam Chandrasekaran, Adam R. Klivans
2024	A*	`conf`	Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. COLT Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos
2024	J	`jnl`	Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. CoRR Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos
2024	A*	`conf`	Testable Learning with Distribution Shift. COLT Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023	A*	`conf`	A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. STOC Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
2023	A*	`conf`	Agnostically Learning Single-Index Models using Omnipredictors. NeurIPS Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
2023	J	`jnl`	Agnostically Learning Single-Index Models using Omnipredictors. CoRR Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
2023	A*	`conf`	Ambient Diffusion: Learning Clean Distributions from Corrupted Data. NeurIPS Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans
2023	J	`jnl`	Ambient Diffusion: Learning Clean Distributions from Corrupted Data. CoRR Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans
2023	J	`jnl`	An Efficient Tester-Learner for Halfspaces. CoRR Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023	A*	`conf`	HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. ICLR Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew D. Ellington, Alex Dimakis, Adam R. Klivans
2023	A	`conf`	ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. DATE Hyunsu Chae, Bhyrav Mutnury, Keren Zhu, Douglas Wallace, Douglas Winterberg, Daniel De Araujo, Jay Reddy, Adam R. Klivans, David Z. Pan
2023	A*	`conf`	Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS Kulin Shah, Sitan Chen, Adam R. Klivans
2023	J	`jnl`	Learning Mixtures of Gaussians Using the DDPM Objective. CoRR Kulin Shah, Sitan Chen, Adam R. Klivans
2023	A*	`conf`	Learning Narrow One-Hidden-Layer ReLU Networks. COLT Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
2023	J	`jnl`	Learning Narrow One-Hidden-Layer ReLU Networks. CoRR Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
2023	A	`conf`	One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers. ICCAD Sriram Ravula, Varun Gorti, Bo Deng, Swagato Chakraborty, James Pingenot, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, Alexandros G. Dimakis
2023	J	`jnl`	One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers. CoRR Sriram Ravula, Varun Gorti, Bo Deng, Swagato Chakraborty, James Pingenot, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, Alexandros G. Dimakis
2023	A*	`conf`	Predicting a Protein's Stability under a Million Mutations. NeurIPS Jeffrey Ouyang-Zhang, Daniel Jesus Diaz, Adam R. Klivans, Philipp Krähenbühl
2023	J	`jnl`	Testable Learning with Distribution Shift. CoRR Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023	A*	`conf`	Tester-Learners for Halfspaces: Universal Algorithms. NeurIPS Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2023	J	`jnl`	Tester-Learners for Halfspaces: Universal Algorithms. CoRR Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
2022	J	`jnl`	A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. CoRR Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
2022	A*	`conf`	Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2022	J	`jnl`	Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2021	J	`jnl`	Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A*	`conf`	Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A*	`conf`	Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A	`conf`	Tight Hardness Results for Training Depth-2 ReLU Networks. ITCS Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman
2020	A*	`conf`	Approximation Schemes for ReLU Regression. COLT Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
2020	J	`jnl`	Approximation Schemes for ReLU Regression. CoRR Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
2020	A*	`conf`	From Boltzmann Machines to Neural Networks and Back Again. NeurIPS Surbhi Goel, Adam R. Klivans, Frederic Koehler
2020	J	`jnl`	From Boltzmann Machines to Neural Networks and Back Again. CoRR Surbhi Goel, Adam R. Klivans, Frederic Koehler
2020	A*	`conf`	Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu
2020	J	`jnl`	Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu
2020	J	`jnl`	Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR Sitan Chen, Adam R. Klivans, Raghu Meka
2020	A*	`conf`	Statistical-Query Lower Bounds via Functional Gradients. NeurIPS Surbhi Goel, Aravind Gollakota, Adam R. Klivans
2020	J	`jnl`	Statistical-Query Lower Bounds via Functional Gradients. CoRR Surbhi Goel, Aravind Gollakota, Adam R. Klivans
2020	A*	`conf`	Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. ICML Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
2020	J	`jnl`	Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
2020	J	`jnl`	The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans
2020	J	`jnl`	Tight Hardness Results for Training Depth-2 ReLU Networks. CoRR Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman
2019	A*	`conf`	Learning Ising Models with Independent Failures. COLT Surbhi Goel, Daniel M. Kane, Adam R. Klivans
2019	J	`jnl`	Learning Ising Models with Independent Failures. CoRR Surbhi Goel, Daniel M. Kane, Adam R. Klivans
2019	A*	`conf`	Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. COLT Surbhi Goel, Adam R. Klivans
2019	J	`jnl`	List-Decodable Linear Regression. CoRR Sushrut Karmalkar, Adam R. Klivans, Pravesh K. Kothari
2019	A*	`conf`	List-decodable Linear Regression. NeurIPS Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari
2019	A*	`conf`	Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. NeurIPS Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans
2019	J	`jnl`	Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. CoRR Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans
2018	A*	`conf`	Efficient Algorithms for Outlier-Robust Regression. COLT Adam R. Klivans, Pravesh K. Kothari, Raghu Meka
2018	J	`jnl`	Efficient Algorithms for Outlier-Robust Regression. CoRR Adam R. Klivans, Pravesh K. Kothari, Raghu Meka
2018	—	`conf`	Hyperparameter optimization: a spectral approach. ICLR (Poster) Elad Hazan, Adam R. Klivans, Yang Yuan
2018	A*	`conf`	Learning One Convolutional Layer with Overlapping Patches. ICML Surbhi Goel, Adam R. Klivans, Raghu Meka
2018	J	`jnl`	Learning One Convolutional Layer with Overlapping Patches. CoRR Surbhi Goel, Adam R. Klivans, Raghu Meka
2018	—	`conf`	Preserving Randomness for Adaptive Algorithms. APPROX-RANDOM William M. Hoza, Adam R. Klivans
2017	—	`conf`	Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. NIPS Surbhi Goel, Adam R. Klivans
2017	J	`jnl`	Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. CoRR Surbhi Goel, Adam R. Klivans
2017	A*	`conf`	Exact MAP Inference by Avoiding Fractional Vertices. ICML Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans
2017	J	`jnl`	Exact MAP Inference by Avoiding Fractional Vertices. CoRR Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans
2017	J	`jnl`	Hyperparameter Optimization: A Spectral Approach. CoRR Elad Hazan, Adam R. Klivans, Yang Yuan
2017	J	`jnl`	Learning Depth-Three Neural Networks in Polynomial Time. CoRR Surbhi Goel, Adam R. Klivans
2017	A*	`conf`	Learning Graphical Models Using Multiplicative Weights. FOCS Adam R. Klivans, Raghu Meka
2017	J	`jnl`	Learning Graphical Models Using Multiplicative Weights. CoRR Adam R. Klivans, Raghu Meka
2017	A*	`conf`	Reliably Learning the ReLU in Polynomial Time. COLT Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler
2016	—	`ch.`	Cryptographic Hardness of Learning. Encyclopedia of Algorithms Adam R. Klivans
2016	J	`jnl`	Preserving Randomness for Adaptive Algorithms. CoRR William M. Hoza, Adam R. Klivans
2016	J	`jnl`	Preserving Randomness for Adaptive Algorithms. Electron. Colloquium Comput. Complex. William M. Hoza, Adam R. Klivans
2016	J	`jnl`	Reliably Learning the ReLU in Polynomial Time. CoRR Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler
2014	J	`jnl`	A Smoothed Analysis for Learning Sparse Polynomials. CoRR Alexandros G. Dimakis, Adam R. Klivans, Murat Kocaoglu, Karthikeyan Shanmugam
2014	J	`jnl`	Bounding the Sensitivity of Polynomial Threshold Functions. Theory Comput. Prahladh Harsha, Adam R. Klivans, Raghu Meka
2014	—	`conf`	Embedding Hard Learning Problems Into Gaussian Space. APPROX-RANDOM Adam R. Klivans, Pravesh Kothari
2014	J	`jnl`	Embedding Hard Learning Problems into Gaussian Space. Electron. Colloquium Comput. Complex. Adam R. Klivans, Pravesh Kothari
2014	—	`conf`	Sparse Polynomial Learning and Graph Sketching. NIPS Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam R. Klivans
2013	A	`conf`	Constructing Hard Functions Using Learning Algorithms. CCC Adam R. Klivans, Pravesh Kothari, Igor C. Oliveira
2013	J	`jnl`	Constructing Hard Functions from Learning Algorithms. Electron. Colloquium Comput. Complex. Adam R. Klivans, Pravesh Kothari, Igor C. Oliveira
2013	A*	`conf`	Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. COLT Daniel M. Kane, Adam R. Klivans, Raghu Meka
2013	J	`jnl`	Moment-Matching Polynomials CoRR Adam R. Klivans, Raghu Meka
2013	J	`jnl`	Moment-Matching Polynomials. Electron. Colloquium Comput. Complex. Adam R. Klivans, Raghu Meka
2012	—	`conf`	An Explicit VC-Theorem for Low-Degree Polynomials. APPROX-RANDOM Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari
2012	J	`jnl`	An Explicit VC-Theorem for Low-Degree Polynomials. Electron. Colloquium Comput. Complex. Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari
2012	J	`jnl`	An invariance principle for polytopes. J. ACM Prahladh Harsha, Adam R. Klivans, Raghu Meka
2012	A*	`conf`	Learning Functions of Halfspaces using Prefix Covers. COLT Parikshit Gopalan, Adam R. Klivans, Raghu Meka
2012	A*	`conf`	Submodular functions are noise stable. SODA Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
2011	A*	`conf`	An FPTAS for #Knapsack and Related Counting Problems. FOCS Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh S. Vempala, Eric Vigoda
2011	J	`jnl`	Submodular Functions Are Noise Stable CoRR Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
2011	J	`jnl`	Submodular Functions Are Noise Stable. Electron. Colloquium Comput. Complex. Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
2010	A*	`conf`	An invariance principle for polytopes. STOC Prahladh Harsha, Adam R. Klivans, Raghu Meka
2010	A*	`conf`	Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC Ilias Diakonikolas, Prahladh Harsha, Adam R. Klivans, Raghu Meka, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan
2010	J	`jnl`	Lower Bounds for Agnostic Learning via Approximate Rank. Comput. Complex. Adam R. Klivans, Alexander A. Sherstov
2010	A*	`conf`	Mansour's Conjecture is True for Random DNF Formulas. COLT Adam R. Klivans, Homin K. Lee, Andrew Wan
2010	J	`jnl`	Mansour's Conjecture is True for Random DNF Formulas. Electron. Colloquium Comput. Complex. Adam R. Klivans, Homin K. Lee, Andrew Wan
2010	J	`jnl`	Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs CoRR Parikshit Gopalan, Adam R. Klivans, Raghu Meka
2010	J	`jnl`	Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. Electron. Colloquium Comput. Complex. Parikshit Gopalan, Adam R. Klivans, Raghu Meka
2009	J	`jnl`	An Invariance Principle for Polytopes CoRR Prahladh Harsha, Adam R. Klivans, Raghu Meka
2009	J	`jnl`	An Invariance Principle for Polytopes. Electron. Colloquium Comput. Complex. Prahladh Harsha, Adam R. Klivans, Raghu Meka
2009	—	`conf`	Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. APPROX-RANDOM Adam R. Klivans, Philip M. Long, Alex K. Tang
2009	J	`jnl`	Bounding the Sensitivity of Polynomial Threshold Functions CoRR Prahladh Harsha, Adam R. Klivans, Raghu Meka
2009	J	`jnl`	Cryptographic hardness for learning intersections of halfspaces. J. Comput. Syst. Sci. Adam R. Klivans, Alexander A. Sherstov
2009	J	`jnl`	Efficient learning algorithms yield circuit lower bounds. J. Comput. Syst. Sci. Lance Fortnow, Adam R. Klivans
2009	—	`conf`	Learning Halfspaces with Malicious Noise. ICALP (1) Adam R. Klivans, Philip M. Long, Rocco A. Servedio
2009	J	`jnl`	Learning Halfspaces with Malicious Noise. J. Mach. Learn. Res. Adam R. Klivans, Philip M. Long, Rocco A. Servedio
2008	A*	`conf`	A Query Algorithm for Agnostically Learning DNF?. COLT Parikshit Gopalan, Adam Kalai, Adam R. Klivans
2008	J	`jnl`	Agnostically Learning Halfspaces. SIAM J. Comput. Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio
2008	A*	`conf`	Agnostically learning decision trees. STOC Parikshit Gopalan, Adam Tauman Kalai, Adam R. Klivans
2008	—	`ch.`	Cryptographic Hardness of Learning. Encyclopedia of Algorithms Adam R. Klivans
2008	A*	`conf`	Learning Geometric Concepts via Gaussian Surface Area. FOCS Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
2008	J	`jnl`	Learning intersections of halfspaces with a margin. J. Comput. Syst. Sci. Adam R. Klivans, Rocco A. Servedio
2008	A*	`conf`	List-decoding reed-muller codes over small fields. STOC Parikshit Gopalan, Adam R. Klivans, David Zuckerman
2008	J	`jnl`	The complexity of properly learning simple concept classes. J. Comput. Syst. Sci. Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi
2007	A*	`conf`	A Lower Bound for Agnostically Learning Disjunctions. COLT Adam R. Klivans, Alexander A. Sherstov
2007	J	`jnl`	Unconditional lower bounds for learning intersections of halfspaces. Mach. Learn. Adam R. Klivans, Alexander A. Sherstov
2006	J	`jnl`	Cryptographic Hardness Results for Learning Intersections of Halfspaces. Electron. Colloquium Comput. Complex. Adam R. Klivans, Alexander A. Sherstov
2006	A*	`conf`	Cryptographic Hardness for Learning Intersections of Halfspaces. FOCS Adam R. Klivans, Alexander A. Sherstov
2006	A*	`conf`	Efficient Learning Algorithms Yield Circuit Lower Bounds. COLT Lance Fortnow, Adam R. Klivans
2006	A*	`conf`	Improved Lower Bounds for Learning Intersections of Halfspaces. COLT Adam R. Klivans, Alexander A. Sherstov
2006	J	`jnl`	Learning Restricted Models of Arithmetic Circuits. Theory Comput. Adam R. Klivans, Amir Shpilka
2006	A	`conf`	Linear Advice for Randomized Logarithmic Space. STACS Lance Fortnow, Adam R. Klivans
2006	J	`jnl`	Toward Attribute Efficient Learning of Decision Lists and Parities. J. Mach. Learn. Res. Adam R. Klivans, Rocco A. Servedio
2005	A*	`conf`	Agnostically Learning Halfspaces. FOCS Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio
2005	J	`jnl`	Linear Advice for Randomized Logarithmic Space Electron. Colloquium Comput. Complex. Lance Fortnow, Adam R. Klivans
2005	A	`conf`	NP with Small Advice. CCC Lance Fortnow, Adam R. Klivans
2004	A*	`conf`	Learnability and Automatizability. FOCS Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi
2004	J	`jnl`	Learning DNF in time 2 J. Comput. Syst. Sci. Adam R. Klivans, Rocco A. Servedio
2004	A*	`conf`	Learning Intersections of Halfspaces with a Margin. COLT Adam R. Klivans, Rocco A. Servedio
2004	J	`jnl`	Learning intersections and thresholds of halfspaces. J. Comput. Syst. Sci. Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
2004	J	`jnl`	NP with Small Advice Electron. Colloquium Comput. Complex. Lance Fortnow, Adam R. Klivans
2004	A*	`conf`	Perceptron-Like Performance for Intersections of Halfspaces. COLT Adam R. Klivans, Rocco A. Servedio
2004	A*	`conf`	Toward Attribute Efficient Learning of Decision Lists and Parities. COLT Adam R. Klivans, Rocco A. Servedio
2003	J	`jnl`	Boosting and Hard-Core Set Construction. Mach. Learn. Adam R. Klivans, Rocco A. Servedio
2003	A*	`conf`	Learning Arithmetic Circuits via Partial Derivatives. COLT Adam R. Klivans, Amir Shpilka
2003	J	`jnl`	Toward Attribute Efficient Learning Algorithms CoRR Adam R. Klivans, Rocco A. Servedio
2002	J	`jnl`	Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. SIAM J. Comput. Adam R. Klivans, Dieter van Melkebeek
2002	A	`conf`	Learnability beyond AC0. CCC Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio
2002	A*	`conf`	Learnability beyond AC0. STOC Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio
2002	A*	`conf`	Learning Intersections and Thresholds of Halfspaces. FOCS Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
2001	A*	`conf`	Learning DNF in time 2 STOC Adam R. Klivans, Rocco A. Servedio
2001	—	`conf`	On the Derandomization of Constant Depth Circuits. RANDOM-APPROX Adam R. Klivans
2001	A*	`conf`	Randomness efficient identity testing of multivariate polynomials. STOC Adam R. Klivans, Daniel A. Spielman
1999	A*	`conf`	Boosting and Hard-Core Sets. FOCS Adam R. Klivans, Rocco A. Servedio
1999	A*	`conf`	Graph Nonisomorphism has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. STOC Adam R. Klivans, Dieter van Melkebeek
1998	J	`jnl`	Graph Nonisomorphism has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. Electron. Colloquium Comput. Complex. Adam R. Klivans, Dieter van Melkebeek