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Adam R. 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
, 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): 2-15 (2024) - [c73]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 - [c72]Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Raghu Meka, Konstantinos Stavropoulos:
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. COLT 2024: 876-922 - [c71]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Testable Learning with Distribution Shift. COLT 2024: 2887-2943 - [c70]Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. COLT 2024: 2944-2978 - [c69]Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. ICLR 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
, 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: 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
, Adam R. Klivans, Raghu Meka:
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
, Alexandros G. Dimakis, Adam R. Klivans:
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
, Adam R. Klivans, Raghu Meka:
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
, Adam R. Klivans, Pravesh Kothari, Homin K. Lee:
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
, Santosh S. Vempala, Eric Vigoda:
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
, 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: 533-542 - [c27]Prahladh Harsha
, Adam R. Klivans, Raghu Meka:
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
, Adam R. Klivans, Toniann Pitassi:
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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