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Adam R. Klivans
Adam Richard Klivans
Person information
- affiliation: University of Texas at Austin, Department of Computer Science, TX, USA
- affiliation: Toyota Technological Institute (TTI), Chicago, IL, USA
- affiliation: Harvard University, Divsion of Engineering and Applied Sciences, Cambridge, MA, USA
- affiliation (PhD 2002): Massachusetts Institute of Technology (MIT), Department of Mathematics, Cambridge, MA, USA
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2020 – today
- 2024
- [j18]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): 35-41 (2024) - [j17]Hyunsu Chae
ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(1): 2-15 (2024) - [c74]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: 644-650 - [c73]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. COLT 2024: 876-922 - [c72]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Testable Learning with Distribution Shift. COLT 2024: 2887-2943 - [c71]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. COLT 2024: 2944-2978 - [c70]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. ICLR 2024 - [c69]Chengyue Gong, Adam R. Klivans, James Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz:
Evolution-Inspired Loss Functions for Protein Representation Learning. ICML 2024 - [i54]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. CoRR abs/2404.02364 (2024) - [i53]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan:
Efficient Discrepancy Testing for Learning with Distribution Shift. CoRR abs/2406.09373 (2024) - [i52]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. CoRR abs/2407.00966 (2024) - 2023
- [c68]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. COLT 2023: 5580-5614 - [c67]Hyunsu Chae
ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design. DATE 2023: 1-6 - [c66]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: 1-9 - [c65]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 - [c64]Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam R. Klivans:
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. NeurIPS 2023 - [c63]Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos:
Agnostically Learning Single-Index Models using Omnipredictors. NeurIPS 2023 - [c62]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. NeurIPS 2023 - [c61]Jeffrey Ouyang-Zhang, Daniel Jesus Diaz, Adam R. Klivans, Philipp Krähenbühl:
Predicting a Protein's Stability under a Million Mutations. NeurIPS 2023 - [c60]Kulin Shah, Sitan Chen, Adam R. Klivans:
Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS 2023 - [c59]Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari:
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. STOC 2023: 1657-1670 - [i51]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. CoRR abs/2302.14853 (2023) - [i50]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR abs/2304.10524 (2023) - [i49]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. CoRR abs/2305.11765 (2023) - [i48]Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam R. Klivans:
Ambient Diffusion: Learning Clean Distributions from Corrupted Data. CoRR abs/2305.19256 (2023) - [i47]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 abs/2306.04001 (2023) - [i46]Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos:
Agnostically Learning Single-Index Models using Omnipredictors. CoRR abs/2306.10615 (2023) - [i45]Kulin Shah, Sitan Chen, Adam R. Klivans:
Learning Mixtures of Gaussians Using the DDPM Objective. CoRR abs/2307.01178 (2023) - [i44]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Testable Learning with Distribution Shift. CoRR abs/2311.15142 (2023) - 2022
- [c58]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS 2022 - [i43]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR abs/2202.05258 (2022) - [i42]Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari:
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. CoRR abs/2211.13312 (2022) - 2021
- [c57]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS 2021: 696-707 - [c56]Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman:
Tight Hardness Results for Training Depth-2 ReLU Networks. ITCS 2021: 22:1-22:14 - [c55]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS 2021: 24087-24098 - [i41]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR abs/2111.04727 (2021) - 2020
- [c54]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
Approximation Schemes for ReLU Regression. COLT 2020: 1452-1485 - [c53]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: 3587-3596 - [c52]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML 2020: 10820-10830 - [c51]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. NeurIPS 2020 - [c50]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. NeurIPS 2020 - [i40]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR abs/2003.01794 (2020) - [i39]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. CoRR abs/2005.12844 (2020) - [i38]Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam R. Klivans:
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent. CoRR abs/2006.12011 (2020) - [i37]Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. CoRR abs/2006.15812 (2020) - [i36]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. CoRR abs/2007.12815 (2020) - [i35]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR abs/2009.13512 (2020) - [i34]Aravind Gollakota, Sushrut Karmalkar, Adam R. Klivans:
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning. CoRR abs/2010.11925 (2020) - [i33]Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman:
Tight Hardness Results for Training Depth-2 ReLU Networks. CoRR abs/2011.13550 (2020)
2010 – 2019
- 2019
- [c49]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. COLT 2019: 1449-1469 - [c48]Surbhi Goel, Adam R. Klivans:
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. COLT 2019: 1470-1499 - [c47]Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari:
List-decodable Linear Regression. NeurIPS 2019: 7423-7432 - [c46]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. NeurIPS 2019: 8582-8591 - [i32]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. CoRR abs/1902.04728 (2019) - [i31]Sushrut Karmalkar, Adam R. Klivans, Pravesh K. Kothari:
List-Decodable Linear Regression. CoRR abs/1905.05679 (2019) - [i30]Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans:
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. CoRR abs/1911.01462 (2019) - 2018
- [c45]William M. Hoza, Adam R. Klivans:
Preserving Randomness for Adaptive Algorithms. APPROX-RANDOM 2018: 43:1-43:19 - [c44]Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. COLT 2018: 1420-1430 - [c43]Elad Hazan, Adam R. Klivans, Yang Yuan:
Hyperparameter optimization: a spectral approach. ICLR (Poster) 2018 - [c42]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. ICML 2018: 1778-1786 - [i29]Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning One Convolutional Layer with Overlapping Patches. CoRR abs/1802.02547 (2018) - [i28]Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. CoRR abs/1803.03241 (2018) - 2017
- [c41]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. COLT 2017: 1004-1042 - [c40]Adam R. Klivans, Raghu Meka:
Learning Graphical Models Using Multiplicative Weights. FOCS 2017: 343-354 - [c39]Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans:
Exact MAP Inference by Avoiding Fractional Vertices. ICML 2017: 2120-2129 - [c38]Surbhi Goel, Adam R. Klivans:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. NIPS 2017: 2192-2202 - [i27]Erik M. Lindgren, Alexandros G. Dimakis, Adam R. Klivans:
Exact MAP Inference by Avoiding Fractional Vertices. CoRR abs/1703.02689 (2017) - [i26]Elad Hazan, Adam R. Klivans, Yang Yuan:
Hyperparameter Optimization: A Spectral Approach. CoRR abs/1706.00764 (2017) - [i25]Adam R. Klivans, Raghu Meka:
Learning Graphical Models Using Multiplicative Weights. CoRR abs/1706.06274 (2017) - [i24]Surbhi Goel, Adam R. Klivans:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. CoRR abs/1708.03708 (2017) - [i23]Surbhi Goel, Adam R. Klivans:
Learning Depth-Three Neural Networks in Polynomial Time. CoRR abs/1709.06010 (2017) - 2016
- [r2]Adam R. Klivans:
Cryptographic Hardness of Learning. Encyclopedia of Algorithms 2016: 475-477 - [i22]William M. Hoza, Adam R. Klivans:
Preserving Randomness for Adaptive Algorithms. CoRR abs/1611.00783 (2016) - [i21]Surbhi Goel, Varun Kanade, Adam R. Klivans, Justin Thaler:
Reliably Learning the ReLU in Polynomial Time. CoRR abs/1611.10258 (2016) - [i20]William M. Hoza, Adam R. Klivans:
Preserving Randomness for Adaptive Algorithms. Electron. Colloquium Comput. Complex. TR16 (2016) - 2014
- [j16]Prahladh Harsha
Bounding the Sensitivity of Polynomial Threshold Functions. Theory Comput. 10: 1-26 (2014) - [c37]Adam R. Klivans, Pravesh Kothari:
Embedding Hard Learning Problems Into Gaussian Space. APPROX-RANDOM 2014: 793-809 - [c36]Murat Kocaoglu, Karthikeyan Shanmugam
Sparse Polynomial Learning and Graph Sketching. NIPS 2014: 3122-3130 - [i19]Alexandros G. Dimakis, Adam R. Klivans, Murat Kocaoglu, Karthikeyan Shanmugam:
A Smoothed Analysis for Learning Sparse Polynomials. CoRR abs/1402.3902 (2014) - [i18]Adam R. Klivans, Pravesh Kothari:
Embedding Hard Learning Problems into Gaussian Space. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [c35]Adam R. Klivans, Pravesh Kothari, Igor C. Oliveira:
Constructing Hard Functions Using Learning Algorithms. CCC 2013: 86-97 - [c34]Daniel M. Kane, Adam R. Klivans, Raghu Meka:
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. COLT 2013: 522-545 - [i17]Adam R. Klivans, Raghu Meka:
Moment-Matching Polynomials. CoRR abs/1301.0820 (2013) - [i16]Adam R. Klivans, Pravesh Kothari, Igor C. Oliveira:
Constructing Hard Functions from Learning Algorithms. Electron. Colloquium Comput. Complex. TR13 (2013) - [i15]Adam R. Klivans, Raghu Meka:
Moment-Matching Polynomials. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [j15]Prahladh Harsha
An invariance principle for polytopes. J. ACM 59(6): 29:1-29:25 (2012) - [c33]Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari:
An Explicit VC-Theorem for Low-Degree Polynomials. APPROX-RANDOM 2012: 495-504 - [c32]Mahdi Cheraghchi
Submodular functions are noise stable. SODA 2012: 1586-1592 - [c31]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Learning Functions of Halfspaces using Prefix Covers. COLT 2012: 15.1-15.10 - [i14]Eshan Chattopadhyay, Adam R. Klivans, Pravesh Kothari:
An Explicit VC-Theorem for Low-Degree Polynomials. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [c30]Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic
An FPTAS for #Knapsack and Related Counting Problems. FOCS 2011: 817-826 - [i13]Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee:
Submodular Functions Are Noise Stable. CoRR abs/1106.0518 (2011) - [i12]Mahdi Cheraghchi, Adam R. Klivans, Pravesh Kothari, Homin K. Lee:
Submodular Functions Are Noise Stable. Electron. Colloquium Comput. Complex. TR11 (2011) - 2010
- [j14]Adam R. Klivans, Alexander A. Sherstov:
Lower Bounds for Agnostic Learning via Approximate Rank. Comput. Complex. 19(4): 581-604 (2010) - [c29]Adam R. Klivans, Homin K. Lee, Andrew Wan:
Mansour's Conjecture is True for Random DNF Formulas. COLT 2010: 368-380 - [c28]Ilias Diakonikolas, Prahladh Harsha
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC 2010: 533-542 - [c27]Prahladh Harsha
An invariance principle for polytopes. STOC 2010: 543-552 - [i11]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. CoRR abs/1008.3187 (2010) - [i10]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. Electron. Colloquium Comput. Complex. TR10 (2010) - [i9]Adam R. Klivans, Homin K. Lee, Andrew Wan:
Mansour's Conjecture is True for Random DNF Formulas. Electron. Colloquium Comput. Complex. TR10 (2010)
2000 – 2009
- 2009
- [j13]Adam R. Klivans, Alexander A. Sherstov:
Cryptographic hardness for learning intersections of halfspaces. J. Comput. Syst. Sci. 75(1): 2-12 (2009) - [j12]Lance Fortnow, Adam R. Klivans:
Efficient learning algorithms yield circuit lower bounds. J. Comput. Syst. Sci. 75(1): 27-36 (2009) - [j11]Adam R. Klivans, Philip M. Long, Rocco A. Servedio:
Learning Halfspaces with Malicious Noise. J. Mach. Learn. Res. 10: 2715-2740 (2009) - [c26]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: 588-600 - [c25]Adam R. Klivans, Philip M. Long, Rocco A. Servedio:
Learning Halfspaces with Malicious Noise. ICALP (1) 2009: 609-621 - [i8]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
Bounding the Sensitivity of Polynomial Threshold Functions. CoRR abs/0909.5175 (2009) - [i7]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An Invariance Principle for Polytopes. CoRR abs/0912.4884 (2009) - [i6]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
An Invariance Principle for Polytopes. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [j10]Michael Alekhnovich, Mark Braverman, Vitaly Feldman
The complexity of properly learning simple concept classes. J. Comput. Syst. Sci. 74(1): 16-34 (2008) - [j9]Adam R. Klivans, Rocco A. Servedio:
Learning intersections of halfspaces with a margin. J. Comput. Syst. Sci. 74(1): 35-48 (2008) - [j8]Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio:
Agnostically Learning Halfspaces. SIAM J. Comput. 37(6): 1777-1805 (2008) - [c24]Parikshit Gopalan, Adam Kalai, Adam R. Klivans:
A Query Algorithm for Agnostically Learning DNF?. COLT 2008: 515-516 - [c23]Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
Learning Geometric Concepts via Gaussian Surface Area. FOCS 2008: 541-550 - [c22]Parikshit Gopalan, Adam R. Klivans, David Zuckerman:
List-decoding reed-muller codes over small fields. STOC 2008: 265-274 - [c21]Parikshit Gopalan, Adam Tauman Kalai, Adam R. Klivans:
Agnostically learning decision trees. STOC 2008: 527-536 - [r1]Adam R. Klivans:
Cryptographic Hardness of Learning. Encyclopedia of Algorithms 2008 - 2007
- [j7]Adam R. Klivans, Alexander A. Sherstov:
Unconditional lower bounds for learning intersections of halfspaces. Mach. Learn. 69(2-3): 97-114 (2007) - [c20]Adam R. Klivans, Alexander A. Sherstov:
A Lower Bound for Agnostically Learning Disjunctions. COLT 2007: 409-423 - 2006
- [j6]Adam R. Klivans, Rocco A. Servedio:
Toward Attribute Efficient Learning of Decision Lists and Parities. J. Mach. Learn. Res. 7: 587-602 (2006) - [j5]Adam R. Klivans, Amir Shpilka:
Learning Restricted Models of Arithmetic Circuits. Theory Comput. 2(10): 185-206 (2006) - [c19]Adam R. Klivans, Alexander A. Sherstov:
Improved Lower Bounds for Learning Intersections of Halfspaces. COLT 2006: 335-349 - [c18]Lance Fortnow, Adam R. Klivans:
Efficient Learning Algorithms Yield Circuit Lower Bounds. COLT 2006: 350-363 - [c17]Adam R. Klivans, Alexander A. Sherstov:
Cryptographic Hardness for Learning Intersections of Halfspaces. FOCS 2006: 553-562 - [c16]Lance Fortnow, Adam R. Klivans:
Linear Advice for Randomized Logarithmic Space. STACS 2006: 469-476 - [i5]Adam R. Klivans, Alexander A. Sherstov:
Cryptographic Hardness Results for Learning Intersections of Halfspaces. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [c15]Lance Fortnow, Adam R. Klivans:
NP with Small Advice. CCC 2005: 228-234 - [c14]Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio:
Agnostically Learning Halfspaces. FOCS 2005: 11-20 - [i4]Lance Fortnow, Adam R. Klivans:
Linear Advice for Randomized Logarithmic Space. Electron. Colloquium Comput. Complex. TR05 (2005) - 2004
- [j4]Adam R. Klivans, Rocco A. Servedio:
Learning DNF in time 2Õ(n1/3). J. Comput. Syst. Sci. 68(2): 303-318 (2004) - [j3]Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
Learning intersections and thresholds of halfspaces. J. Comput. Syst. Sci. 68(4): 808-840 (2004) - [c13]Adam R. Klivans, Rocco A. Servedio:
Toward Attribute Efficient Learning of Decision Lists and Parities. COLT 2004: 224-238 - [c12]Adam R. Klivans, Rocco A. Servedio:
Learning Intersections of Halfspaces with a Margin. COLT 2004: 348-362 - [c11]Adam R. Klivans, Rocco A. Servedio:
Perceptron-Like Performance for Intersections of Halfspaces. COLT 2004: 639-640 - [c10]Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi:
Learnability and Automatizability. FOCS 2004: 621-630 - [i3]Lance Fortnow, Adam R. Klivans:
NP with Small Advice. Electron. Colloquium Comput. Complex. TR04 (2004) - 2003
- [j2]Adam R. Klivans, Rocco A. Servedio:
Boosting and Hard-Core Set Construction. Mach. Learn. 51(3): 217-238 (2003) - [c9]Adam R. Klivans, Amir Shpilka
Learning Arithmetic Circuits via Partial Derivatives. COLT 2003: 463-476 - [i2]Adam R. Klivans, Rocco A. Servedio:
Toward Attribute Efficient Learning Algorithms. CoRR cs.LG/0311042 (2003) - 2002
- [j1]Adam R. Klivans, Dieter van Melkebeek:
Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. SIAM J. Comput. 31(5): 1501-1526 (2002) - [c8]Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio:
Learnability beyond AC0. CCC 2002: 26 - [c7]Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
Learning Intersections and Thresholds of Halfspaces. FOCS 2002: 177-186 - [c6]Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio:
Learnability beyond AC0. STOC 2002: 776-784 - 2001
- [c5]Adam R. Klivans:
On the Derandomization of Constant Depth Circuits. RANDOM-APPROX 2001: 249-260 - [c4]Adam R. Klivans, Daniel A. Spielman:
Randomness efficient identity testing of multivariate polynomials. STOC 2001: 216-223 - [c3]Adam R. Klivans, Rocco A. Servedio:
Learning DNF in time 2Õ(n1/3). STOC 2001: 258-265
1990 – 1999
- 1999
- [c2]Adam R. Klivans, Rocco A. Servedio:
Boosting and Hard-Core Sets. FOCS 1999: 624-633 - [c1]Adam R. Klivans, Dieter van Melkebeek:
Graph Nonisomorphism has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. STOC 1999: 659-667 - 1998
- [i1]Adam R. Klivans, Dieter van Melkebeek:
Graph Nonisomorphism has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses. Electron. Colloquium Comput. Complex. TR98 (1998)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from .
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last updated on 2024-09-13 00:44 CEST by the dblp team
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