Boosting and Hard-Core Sets. - Adam R. Klivans; Rocco A. Servedio (FOCS, 1999)

On the Derandomization of Constant Depth Circuits. - Adam R. Klivans (RANDOM-APPROX, 2001)

Randomness efficient identity testing of multivariate polynomials. - Adam R. Klivans; Daniel A. Spielman (STOC, 2001)

Learnability beyond AC0. - Jeffrey C. Jackson; Adam R. Klivans; Rocco A. Servedio (STOC, 2002)

Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. - Adam R. Klivans; Dieter van Melkebeek (SIAM J. Comput., 2002)

Learning Arithmetic Circuits via Partial Derivatives. - Adam R. Klivans; Amir Shpilka (COLT, 2003)

Toward Attribute Efficient Learning Algorithms - Adam R. Klivans; Rocco A. Servedio (CoRR, 2003)

Boosting and Hard-Core Set Construction. - Adam R. Klivans; Rocco A. Servedio (Mach. Learn., 2003)

Perceptron-Like Performance for Intersections of Halfspaces. - Adam R. Klivans; Rocco A. Servedio (COLT, 2004)

Learnability and Automatizability. - Michael Alekhnovich; Mark Braverman; Vitaly Feldman; Adam R. Klivans; Toniann Pitassi (FOCS, 2004)

NP with Small Advice - Lance Fortnow; Adam R. Klivans (Electron. Colloquium Comput. Complex., 2004)

Learning intersections and thresholds of halfspaces. - Adam R. Klivans; Ryan O'Donnell; Rocco A. Servedio (J. Comput. Syst. Sci., 2004)

Learning DNF in time 2Õ(n1/3). - Adam R. Klivans; Rocco A. Servedio (J. Comput. Syst. Sci., 2004)

Linear Advice for Randomized Logarithmic Space - Lance Fortnow; Adam R. Klivans (Electron. Colloquium Comput. Complex., 2005)

Improved Lower Bounds for Learning Intersections of Halfspaces. - Adam R. Klivans; Alexander A. Sherstov (COLT, 2006)

Cryptographic Hardness Results for Learning Intersections of Halfspaces. - Adam R. Klivans; Alexander A. Sherstov (Electron. Colloquium Comput. Complex., 2006)

Toward Attribute Efficient Learning of Decision Lists and Parities. - Adam R. Klivans; Rocco A. Servedio (J. Mach. Learn. Res., 2006)

Learning Restricted Models of Arithmetic Circuits. - Adam R. Klivans; Amir Shpilka (Theory Comput., 2006)

A Lower Bound for Agnostically Learning Disjunctions. - Adam R. Klivans; Alexander A. Sherstov (COLT, 2007)

Unconditional lower bounds for learning intersections of halfspaces. - Adam R. Klivans; Alexander A. Sherstov (Mach. Learn., 2007)

A Query Algorithm for Agnostically Learning DNF?. - Parikshit Gopalan; Adam Kalai; Adam R. Klivans (COLT, 2008)

Learning Geometric Concepts via Gaussian Surface Area. - Adam R. Klivans; Ryan O'Donnell; Rocco A. Servedio (FOCS, 2008)

Agnostically learning decision trees. - Parikshit Gopalan; Adam Tauman Kalai; Adam R. Klivans (STOC, 2008)

List-decoding reed-muller codes over small fields. - Parikshit Gopalan; Adam R. Klivans; David Zuckerman (STOC, 2008)

The complexity of properly learning simple concept classes. - Michael Alekhnovich; Mark Braverman; Vitaly Feldman; Adam R. Klivans; Toniann Pitassi (J. Comput. Syst. Sci., 2008)

Learning intersections of halfspaces with a margin. - Adam R. Klivans; Rocco A. Servedio (J. Comput. Syst. Sci., 2008)

Agnostically Learning Halfspaces. - Adam Tauman Kalai; Adam R. Klivans; Yishay Mansour; Rocco A. Servedio (SIAM J. Comput., 2008)

Cryptographic Hardness of Learning. - Adam R. Klivans (Encyclopedia of Algorithms, 2008)

Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. - Adam R. Klivans; Philip M. Long; Alex K. Tang (APPROX-RANDOM, 2009)

An Invariance Principle for Polytopes. - Prahladh Harsha; Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2009)

Efficient learning algorithms yield circuit lower bounds. - Lance Fortnow; Adam R. Klivans (J. Comput. Syst. Sci., 2009)

Cryptographic hardness for learning intersections of halfspaces. - Adam R. Klivans; Alexander A. Sherstov (J. Comput. Syst. Sci., 2009)

Learning Halfspaces with Malicious Noise. - Adam R. Klivans; Philip M. Long; Rocco A. Servedio (J. Mach. Learn. Res., 2009)

Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. - Ilias Diakonikolas; Prahladh Harsha; Adam R. Klivans; Raghu Meka; Prasad Raghavendra; Rocco A. Servedio; Li-Yang Tan (STOC, 2010)

Lower Bounds for Agnostic Learning via Approximate Rank. - Adam R. Klivans; Alexander A. Sherstov (Comput. Complex., 2010)

Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2010)

Mansour's Conjecture is True for Random DNF Formulas. - Adam R. Klivans; Homin K. Lee; Andrew Wan (Electron. Colloquium Comput. Complex., 2010)

An FPTAS for #Knapsack and Related Counting Problems. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka; Daniel Stefankovic; Santosh S. Vempala; Eric Vigoda (FOCS, 2011)

Submodular Functions Are Noise Stable. - Mahdi Cheraghchi; Adam R. Klivans; Pravesh Kothari; Homin K. Lee (Electron. Colloquium Comput. Complex., 2011)

An Explicit VC-Theorem for Low-Degree Polynomials. - Eshan Chattopadhyay; Adam R. Klivans; Pravesh Kothari (Electron. Colloquium Comput. Complex., 2012)

Learning Functions of Halfspaces using Prefix Covers. - Parikshit Gopalan; Adam R. Klivans; Raghu Meka (COLT, 2012)

Constructing Hard Functions Using Learning Algorithms. - Adam R. Klivans; Pravesh Kothari; Igor C. Oliveira 001 (CCC, 2013)

Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. - Daniel M. Kane; Adam R. Klivans; Raghu Meka (COLT, 2013)

Constructing Hard Functions from Learning Algorithms. - Adam R. Klivans; Pravesh Kothari; Igor C. Oliveira 001 (Electron. Colloquium Comput. Complex., 2013)

Moment-Matching Polynomials. - Adam R. Klivans; Raghu Meka (Electron. Colloquium Comput. Complex., 2013)

Sparse Polynomial Learning and Graph Sketching. - Murat Kocaoglu; Karthikeyan Shanmugam; Alexandros G. Dimakis; Adam R. Klivans (NIPS, 2014)

A Smoothed Analysis for Learning Sparse Polynomials. - Alexandros G. Dimakis; Adam R. Klivans; Murat Kocaoglu; Karthikeyan Shanmugam (CoRR, 2014)

Embedding Hard Learning Problems into Gaussian Space. - Adam R. Klivans; Pravesh Kothari (Electron. Colloquium Comput. Complex., 2014)

Bounding the Sensitivity of Polynomial Threshold Functions. - Prahladh Harsha; Adam R. Klivans; Raghu Meka (Theory Comput., 2014)

Preserving Randomness for Adaptive Algorithms. - William M. Hoza; Adam R. Klivans (Electron. Colloquium Comput. Complex., 2016)

Cryptographic Hardness of Learning. - Adam R. Klivans (Encyclopedia of Algorithms, 2016)

Reliably Learning the ReLU in Polynomial Time. - Surbhi Goel; Varun Kanade; Adam R. Klivans; Justin Thaler (COLT, 2017)

Learning Graphical Models Using Multiplicative Weights. - Adam R. Klivans; Raghu Meka (FOCS, 2017)

Exact MAP Inference by Avoiding Fractional Vertices. - Erik M. Lindgren; Alexandros G. Dimakis; Adam R. Klivans (ICML, 2017)

Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. - Surbhi Goel; Adam R. Klivans (NIPS, 2017)

Learning Depth-Three Neural Networks in Polynomial Time. - Surbhi Goel; Adam R. Klivans (CoRR, 2017)

Efficient Algorithms for Outlier-Robust Regression. - Adam R. Klivans; Pravesh K. Kothari; Raghu Meka (COLT, 2018)

Hyperparameter optimization: a spectral approach. - Elad Hazan; Adam R. Klivans; Yang Yuan (ICLR (Poster), 2018)

Learning One Convolutional Layer with Overlapping Patches. - Surbhi Goel; Adam R. Klivans; Raghu Meka (ICML, 2018)

Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. - Surbhi Goel; Adam R. Klivans (COLT, 2019)

Learning Ising Models with Independent Failures. - Surbhi Goel; Daniel M. Kane; Adam R. Klivans (COLT, 2019)

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. - Surbhi Goel; Sushrut Karmalkar; Adam R. Klivans (NeurIPS, 2019)

List-decodable Linear Regression. - Sushrut Karmalkar; Adam R. Klivans; Pravesh Kothari (NeurIPS, 2019)

Approximation Schemes for ReLU Regression. - Ilias Diakonikolas; Surbhi Goel; Sushrut Karmalkar; Adam R. Klivans; Mahdi Soltanolkotabi (COLT, 2020)

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. - Surbhi Goel; Aravind Gollakota; Zhihan Jin; Sushrut Karmalkar; Adam R. Klivans (ICML, 2020)

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. - Mao Ye 006; Chengyue Gong; Lizhen Nie; Denny Zhou; Adam R. Klivans; Qiang Liu 001 (ICML, 2020)

Statistical-Query Lower Bounds via Functional Gradients. - Surbhi Goel; Aravind Gollakota; Adam R. Klivans (NeurIPS, 2020)

From Boltzmann Machines to Neural Networks and Back Again. - Surbhi Goel; Adam R. Klivans; Frederic Koehler (NeurIPS, 2020)

The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. - Aravind Gollakota; Sushrut Karmalkar; Adam R. Klivans (CoRR, 2020)

Learning Deep ReLU Networks Is Fixed-Parameter Tractable. - Sitan Chen; Adam R. Klivans; Raghu Meka (FOCS, 2021)

Tight Hardness Results for Training Depth-2 ReLU Networks. - Surbhi Goel; Adam R. Klivans; Pasin Manurangsi; Daniel Reichman 001 (ITCS, 2021)

Efficiently Learning One Hidden Layer ReLU Networks From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (NeurIPS, 2021)

Efficiently Learning Any One Hidden Layer ReLU Network From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (CoRR, 2021)

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

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

One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers. - Sriram Ravula; Varun Gorti; Bo Deng; Swagato Chakraborty; James Pingenot; Bhyrav Mutnury; Douglas Wallace; Douglas Winterberg; Adam R. Klivans; Alexandros G. Dimakis (ICCAD, 2023)

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. - Tianlong Chen 001; Chengyue Gong; Daniel Jesus Diaz; Xuxi Chen; Jordan Tyler Wells; Qiang Liu 001; Zhangyang Wang; Andrew D. Ellington; Alex Dimakis; Adam R. Klivans (ICLR, 2023)

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

Agnostically Learning Single-Index Models using Omnipredictors. - Aravind Gollakota; Parikshit Gopalan; Adam R. Klivans; Konstantinos Stavropoulos (NeurIPS, 2023)

Tester-Learners for Halfspaces: Universal Algorithms. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2023)

Predicting a Protein's Stability under a Million Mutations. - Jeffrey Ouyang-Zhang; Daniel Jesus Diaz; Adam R. Klivans; Philipp Krähenbühl (NeurIPS, 2023)

Learning Mixtures of Gaussians Using the DDPM Objective. - Kulin Shah; Sitan Chen; Adam R. Klivans (NeurIPS, 2023)

A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. - Aravind Gollakota; Adam R. Klivans; Pravesh K. Kothari (STOC, 2023)

ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning. - Hyunsu Chae; Keren Zhu 001; Bhyrav Mutnury; Zixuan Jiang; Daniel De Araujo; Douglas Wallace; Douglas Winterberg; Adam R. Klivans; David Z. Pan (ASPDAC, 2024)

Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. - Gautam Chandrasekaran; Adam R. Klivans; Vasilis Kontonis; Raghu Meka; Konstantinos Stavropoulos (COLT, 2024)

Testable Learning with Distribution Shift. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2024)

Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2024)

An Efficient Tester-Learner for Halfspaces. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (ICLR, 2024)

Evolution-Inspired Loss Functions for Protein Representation Learning. - Chengyue Gong; Adam R. Klivans; James Loy; Tianlong Chen 001; Qiang Liu 001; Daniel Jesus Diaz (ICML, 2024)

Efficient Discrepancy Testing for Learning with Distribution Shift. - Gautam Chandrasekaran; Adam R. Klivans; Vasilis Kontonis; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2024)

Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world. - Adam R. Klivans; Alexandros G. Dimakis; Kristen Grauman; Jonathan I. Tamir; Daniel Jesus Diaz; Karen Davidson (AI Mag., 2024)

ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. - Hyunsu Chae; Keren Zhu 001; Bhyrav Mutnury; Douglas Wallace; Douglas Winterberg; Daniel De Araujo; Jay Reddy; Adam R. Klivans; David Z. Pan (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2024)

Learning Constant-Depth Circuits in Malicious Noise Models. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2025)

Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees. - Gautam Chandrasekaran; Adam R. Klivans; Lin Lin Lee; Konstantinos Stavropoulos (ICLR, 2025)

Distilling Structural Representations into Protein Sequence Models. - Jeffrey Ouyang-Zhang; Chengyue Gong; Yue Zhao 006; Philipp Krähenbühl; Adam R. Klivans; Daniel Jesus Diaz (ICLR, 2025)

Learning the Sherrington-Kirkpatrick Model Even at Low Temperature. - Gautam Chandrasekaran; Adam R. Klivans (STOC, 2025)

Testing Noise Assumptions of Learning Algorithms. - Surbhi Goel; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (CoRR, 2025)

Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. - Kulin Shah; Alkis Kalavasis; Adam R. Klivans; Giannis Daras (CoRR, 2025)

The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination. - Adam R. Klivans; Konstantinos Stavropoulos; Kevin Tian; Arsen Vasilyan (CoRR, 2025)

Learning Juntas under Markov Random Fields. - Gautam Chandrasekaran; Adam R. Klivans (CoRR, 2025)

Ambient Diffusion Omni: Training Good Models with Bad Data. - Giannis Daras; Adrián Rodríguez-Muñoz; Adam R. Klivans; Antonio Torralba 001; Constantinos Daskalakis (CoRR, 2025)