Adam R. Klivans

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 70

Top Venues

CoRR

NeurIPS

COLT

ICML

Publications

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Testable Learning with Distribution Shift. COLT (2024)
Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan
Efficient Discrepancy Testing for Learning with Distribution Shift. CoRR (2024)
Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. CoRR (2024)
Adam R. Klivans, Alexandros G. Dimakis, Kristen Grauman, Jonathan I. Tamir, Daniel Jesus Diaz, Karen Davidson
Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world. AI Mag. 45 (1) (2024)
Hyunsu Chae, Keren Zhu, Bhyrav Mutnury, Zixuan Jiang, Daniel De Araujo, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, David Z. Pan
ISOP-Yield: Yield-Aware Stack-Up Optimization for Advanced Package using Machine Learning. ASPDAC (2024)
Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. COLT (2024)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
An Efficient Tester-Learner for Halfspaces. ICLR (2024)
Hyunsu Chae, Keren Zhu, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Daniel De Araujo, Jay Reddy, Adam R. Klivans, David Z. Pan
ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43 (1) (2024)
Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. CoRR (2024)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Tester-Learners for Halfspaces: Universal Algorithms. NeurIPS (2023)
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. STOC (2023)
Kulin Shah, Sitan Chen, Adam R. Klivans
Learning Mixtures of Gaussians Using the DDPM Objective. CoRR (2023)
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
Agnostically Learning Single-Index Models using Omnipredictors. NeurIPS (2023)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Tester-Learners for Halfspaces: Universal Algorithms. CoRR (2023)
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. NeurIPS (2023)
Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Testable Learning with Distribution Shift. CoRR (2023)
Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos
Agnostically Learning Single-Index Models using Omnipredictors. CoRR (2023)
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
An Efficient Tester-Learner for Halfspaces. CoRR (2023)
Sriram Ravula, Varun Gorti, Bo Deng, Swagato Chakraborty, James Pingenot, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, Alexandros G. Dimakis
One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers. CoRR (2023)
Sriram Ravula, Varun Gorti, Bo Deng, Swagato Chakraborty, James Pingenot, Bhyrav Mutnury, Douglas Wallace, Douglas Winterberg, Adam R. Klivans, Alexandros G. Dimakis
One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers. ICCAD (2023)
Hyunsu Chae, Bhyrav Mutnury, Keren Zhu, Douglas Wallace, Douglas Winterberg, Daniel De Araujo, Jay Reddy, Adam R. Klivans, David Z. Pan
ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. DATE (2023)
Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR (2023)
Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning Narrow One-Hidden-Layer ReLU Networks. COLT (2023)
Kulin Shah, Sitan Chen, Adam R. Klivans
Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS (2023)
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. CoRR (2023)
Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew D. Ellington, Alex Dimakis, Adam R. Klivans
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. ICLR (2023)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR (2022)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS (2022)
Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. CoRR (2022)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS (2022)
Sitan Chen, Adam R. Klivans, Raghu Meka
Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR (2021)
Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman
Tight Hardness Results for Training Depth-2 ReLU Networks. ITCS (2021)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS (2021)
Sitan Chen, Adam R. Klivans, Raghu Meka
Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS (2021)
Surbhi Goel, Adam R. Klivans, Frederic Koehler
From Boltzmann Machines to Neural Networks and Back Again. NeurIPS (2020)
Surbhi Goel, Adam R. Klivans, Frederic Koehler
From Boltzmann Machines to Neural Networks and Back Again. CoRR (2020)
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
Statistical-Query Lower Bounds via Functional Gradients. CoRR (2020)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR (2020)
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
Approximation Schemes for ReLU Regression. CoRR (2020)
Surbhi Goel, Aravind Gollakota, Adam R. Klivans
Statistical-Query Lower Bounds via Functional Gradients. NeurIPS (2020)
Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman
Tight Hardness Results for Training Depth-2 ReLU Networks. CoRR (2020)
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. ICML (2020)
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
Approximation Schemes for ReLU Regression. COLT (2020)
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR (2020)
Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR (2020)
Surbhi Goel, Adam R. Klivans
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. COLT (2019)
Surbhi Goel, Daniel M. Kane, Adam R. Klivans
Learning Ising Models with Independent Failures. COLT (2019)
Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari
List-decodable Linear Regression. NeurIPS (2019)
Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. CoRR (2019)
Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. NeurIPS (2019)
Sushrut Karmalkar, Adam R. Klivans, Pravesh K. Kothari
List-Decodable Linear Regression. CoRR (2019)
Surbhi Goel, Daniel M. Kane, Adam R. Klivans
Learning Ising Models with Independent Failures. CoRR (2019)
Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning One Convolutional Layer with Overlapping Patches. ICML (2018)
Adam R. Klivans, Pravesh K. Kothari, Raghu Meka
Efficient Algorithms for Outlier-Robust Regression. CoRR (2018)
William M. Hoza, Adam R. Klivans
Preserving Randomness for Adaptive Algorithms. APPROX-RANDOM (2018)
Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning One Convolutional Layer with Overlapping Patches. CoRR (2018)
Adam R. Klivans, Pravesh K. Kothari, Raghu Meka
Efficient Algorithms for Outlier-Robust Regression. COLT (2018)
Elad Hazan, Adam R. Klivans, Yang Yuan
Hyperparameter optimization: a spectral approach. ICLR (Poster) (2018)
Surbhi Goel, Adam R. Klivans
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. NIPS (2017)
Adam R. Klivans, Raghu Meka
Learning Graphical Models Using Multiplicative Weights. FOCS (2017)
Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans
Exact MAP Inference by Avoiding Fractional Vertices. CoRR (2017)
Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler
Reliably Learning the ReLU in Polynomial Time. COLT (2017)
Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans
Exact MAP Inference by Avoiding Fractional Vertices. ICML (2017)
Surbhi Goel, Adam R. Klivans
Learning Depth-Three Neural Networks in Polynomial Time. CoRR (2017)
Elad Hazan, Adam R. Klivans, Yang Yuan
Hyperparameter Optimization: A Spectral Approach. CoRR (2017)
Surbhi Goel, Adam R. Klivans
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. CoRR (2017)
Adam R. Klivans, Raghu Meka
Learning Graphical Models Using Multiplicative Weights. CoRR (2017)
William M. Hoza, Adam R. Klivans
Preserving Randomness for Adaptive Algorithms. CoRR (2016)
William M. Hoza, Adam R. Klivans
Preserving Randomness for Adaptive Algorithms. Electron. Colloquium Comput. Complex. 23 (2016)
Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler
Reliably Learning the ReLU in Polynomial Time. CoRR (2016)
Adam R. Klivans, Pravesh Kothari
Embedding Hard Learning Problems into Gaussian Space. Electron. Colloquium Comput. Complex. 21 (2014)
Prahladh Harsha, Adam R. Klivans, Raghu Meka
Bounding the Sensitivity of Polynomial Threshold Functions. Theory Comput. 10 (2014)
Alexandros G. Dimakis, Adam R. Klivans, Murat Kocaoglu, Karthikeyan Shanmugam
A Smoothed Analysis for Learning Sparse Polynomials. CoRR (2014)
Adam R. Klivans, Pravesh Kothari
Embedding Hard Learning Problems Into Gaussian Space. APPROX-RANDOM (2014)
Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam R. Klivans
Sparse Polynomial Learning and Graph Sketching. NIPS (2014)
Adam R. Klivans, Raghu Meka
Moment-Matching Polynomials. Electron. Colloquium Comput. Complex. 20 (2013)
Adam R. Klivans, Raghu Meka
Moment-Matching Polynomials CoRR (2013)
Adam R. Klivans, Pravesh Kothari, Igor Carboni Oliveira
Constructing Hard Functions Using Learning Algorithms. Computational Complexity Conference (2013)
Daniel M. Kane, Adam R. Klivans, Raghu Meka
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. COLT (2013)
Adam R. Klivans, Pravesh Kothari, Igor Carboni Oliveira
Constructing Hard Functions from Learning Algorithms. Electron. Colloquium Comput. Complex. 20 (2013)
Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari
An Explicit VC-Theorem for Low-Degree Polynomials. APPROX-RANDOM (2012)
Prahladh Harsha, Adam R. Klivans, Raghu Meka
An invariance principle for polytopes. J. ACM 59 (6) (2012)
Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari
An Explicit VC-Theorem for Low-Degree Polynomials. Electron. Colloquium Comput. Complex. 19 (2012)
Parikshit Gopalan, Adam R. Klivans, Raghu Meka
Learning Functions of Halfspaces using Prefix Covers. COLT (2012)
Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
Submodular functions are noise stable. SODA (2012)
Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh S. Vempala, Eric Vigoda
An FPTAS for #Knapsack and Related Counting Problems. FOCS (2011)
Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
Submodular Functions Are Noise Stable. Electron. Colloquium Comput. Complex. 18 (2011)
Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee
Submodular Functions Are Noise Stable CoRR (2011)
Ilias Diakonikolas, Prahladh Harsha, Adam R. Klivans, Raghu Meka, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC (2010)
Parikshit Gopalan, Adam R. Klivans, Raghu Meka
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs CoRR (2010)
Adam R. Klivans, Homin K. Lee, Andrew Wan
Mansour's Conjecture is True for Random DNF Formulas. Electron. Colloquium Comput. Complex. 17 (2010)
Prahladh Harsha, Adam R. Klivans, Raghu Meka
An invariance principle for polytopes. STOC (2010)
Adam R. Klivans, Alexander A. Sherstov
Lower Bounds for Agnostic Learning via Approximate Rank. Comput. Complex. 19 (4) (2010)
Adam R. Klivans, Homin K. Lee, Andrew Wan
Mansour's Conjecture is True for Random DNF Formulas. COLT (2010)
Parikshit Gopalan, Adam R. Klivans, Raghu Meka
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. Electron. Colloquium Comput. Complex. 17 (2010)
Prahladh Harsha, Adam R. Klivans, Raghu Meka
An Invariance Principle for Polytopes CoRR (2009)
Adam R. Klivans, Philip M. Long, Rocco A. Servedio
Learning Halfspaces with Malicious Noise. ICALP (1) (2009)
Adam R. Klivans, Alexander A. Sherstov
Cryptographic hardness for learning intersections of halfspaces. J. Comput. Syst. Sci. 75 (1) (2009)
Prahladh Harsha, Adam R. Klivans, Raghu Meka
Bounding the Sensitivity of Polynomial Threshold Functions CoRR (2009)
Adam R. Klivans, Philip M. Long, Alex K. Tang
Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. APPROX-RANDOM (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Adam R. Klivans