Adam R. Klivans
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
60
Venues
11
Active years
1999–2026
Best venue rank
A*
Where they publish
Papers
60 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Testing Noise Assumptions of Learning Algorithms. | Surbhi Goel, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2026 | COLT | Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension. | Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2026 | COLT | Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift. | Shyamal Patel, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2026 | STOC | A Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube. | Gautam Chandrasekaran, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2025 | COLT | Learning Constant-Depth Circuits in Malicious Noise Models. | Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2025 | ICLR | Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees. | Gautam Chandrasekaran, Adam R. Klivans, Lin Lin Lee, Konstantinos Stavropoulos |
| 2025 | ICLR | Distilling Structural Representations into Protein Sequence Models. | Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Krhenbhl, Adam R. Klivans, Daniel Jesus Diaz |
| 2025 | ICML | Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. | Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras |
| 2025 | STOC | Learning the Sherrington-Kirkpatrick Model Even at Low Temperature. | Gautam Chandrasekaran, Adam R. Klivans |
| 2024 | ASPDAC | ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning. | Hyunsu Chae, Keren Zhu, Bhyrav Mutnury, Zixuan Jiang, Daniel De Araujo, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, David Z. Pan |
| 2024 | COLT | Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. | Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos |
| 2024 | COLT | Testable Learning with Distribution Shift. | Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2024 | COLT | Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. | Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2024 | ICLR | An Efficient Tester-Learner for Halfspaces. | Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2024 | ICML | Evolution-Inspired Loss Functions for Protein Representation Learning. | Chengyue Gong, Adam R. Klivans, James Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz |
| 2023 | COLT | Learning Narrow One-Hidden-Layer ReLU Networks. | Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2023 | DATE | ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. | Hyunsu Chae, Bhyrav Mutnury, Keren Zhu, Douglas Wallace, Douglas Winterberg, Daniel De Araujo, Jay Reddy, Adam R. Klivans, David Z. Pan |
| 2023 | ICCAD | 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 |
| 2023 | ICLR | 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 D. Ellington, Alex Dimakis, Adam R. Klivans |
| 2023 | STOC | A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. | Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari |
| 2021 | FOCS | Learning Deep ReLU Networks Is Fixed-Parameter Tractable. | Sitan Chen, Adam R. Klivans, Raghu Meka |
| 2020 | COLT | Approximation Schemes for ReLU Regression. | Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi |
| 2020 | ICML | Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. | Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans |
| 2020 | ICML | Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. | Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu |
| 2019 | COLT | Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. | Surbhi Goel, Adam R. Klivans |
| 2019 | COLT | Learning Ising Models with Independent Failures. | Surbhi Goel, Daniel M. Kane, Adam R. Klivans |
| 2018 | COLT | Efficient Algorithms for Outlier-Robust Regression. | Adam R. Klivans, Pravesh K. Kothari, Raghu Meka |
| 2018 | ICLR | Hyperparameter optimization: a spectral approach. | Elad Hazan, Adam R. Klivans, Yang Yuan |
| 2018 | ICML | Learning One Convolutional Layer with Overlapping Patches. | Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2017 | COLT | Reliably Learning the ReLU in Polynomial Time. | Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler |
| 2017 | FOCS | Learning Graphical Models Using Multiplicative Weights. | Adam R. Klivans, Raghu Meka |
| 2017 | ICML | Exact MAP Inference by Avoiding Fractional Vertices. | Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans |
| 2013 | COLT | Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. | Daniel M. Kane, Adam R. Klivans, Raghu Meka |
| 2012 | SODA | Submodular functions are noise stable. | Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee |
| 2011 | FOCS | An FPTAS for #Knapsack and Related Counting Problems. | Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh S. Vempala, Eric Vigoda |
| 2010 | COLT | Mansour's Conjecture is True for Random DNF Formulas. | Adam R. Klivans, Homin K. Lee, Andrew Wan |
| 2010 | STOC | 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 |
| 2010 | STOC | An invariance principle for polytopes. | Prahladh Harsha, Adam R. Klivans, Raghu Meka |
| 2009 | ICALP | Learning Halfspaces with Malicious Noise. | Adam R. Klivans, Philip M. Long, Rocco A. Servedio |
| 2008 | COLT | A Query Algorithm for Agnostically Learning DNF?. | Parikshit Gopalan, Adam Kalai, Adam R. Klivans |
| 2008 | FOCS | Learning Geometric Concepts via Gaussian Surface Area. | Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio |
| 2008 | STOC | Agnostically learning decision trees. | Parikshit Gopalan, Adam Tauman Kalai, Adam R. Klivans |
| 2008 | STOC | List-decoding reed-muller codes over small fields. | Parikshit Gopalan, Adam R. Klivans, David Zuckerman |
| 2007 | COLT | A Lower Bound for Agnostically Learning Disjunctions. | Adam R. Klivans, Alexander A. Sherstov |
| 2006 | COLT | Efficient Learning Algorithms Yield Circuit Lower Bounds. | Lance Fortnow, Adam R. Klivans |
| 2006 | COLT | Improved Lower Bounds for Learning Intersections of Halfspaces. | Adam R. Klivans, Alexander A. Sherstov |
| 2006 | FOCS | Cryptographic Hardness for Learning Intersections of Halfspaces. | Adam R. Klivans, Alexander A. Sherstov |
| 2006 | STACS | Linear Advice for Randomized Logarithmic Space. | Lance Fortnow, Adam R. Klivans |
| 2005 | FOCS | Agnostically Learning Halfspaces. | Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio |
| 2004 | COLT | Learning Intersections of Halfspaces with a Margin. | Adam R. Klivans, Rocco A. Servedio |
| 2004 | COLT | Perceptron-Like Performance for Intersections of Halfspaces. | Adam R. Klivans, Rocco A. Servedio |
| 2004 | COLT | Toward Attribute Efficient Learning of Decision Lists and Parities. | Adam R. Klivans, Rocco A. Servedio |
| 2004 | FOCS | Learnability and Automatizability. | Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi |
| 2003 | COLT | Learning Arithmetic Circuits via Partial Derivatives. | Adam R. Klivans, Amir Shpilka |
| 2002 | FOCS | Learning Intersections and Thresholds of Halfspaces. | Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio |
| 2002 | STOC | Learnability beyond AC0. | Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio |
| 2001 | STOC | Randomness efficient identity testing of multivariate polynomials. | Adam R. Klivans, Daniel A. Spielman |
| 2001 | STOC | Learning DNF in time 2 | Adam R. Klivans, Rocco A. Servedio |
| 1999 | FOCS | Boosting and Hard-Core Sets. | Adam R. Klivans, Rocco A. Servedio |
| 1999 | STOC | Graph Nonisomorphism has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. | Adam R. Klivans, Dieter van Melkebeek |