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Semantic ScholarAdam R. Klivans
Person information
- affiliation: University of Texas at Austin, Department of Computer Science
- affiliation: Toyota Technological Institute (TTI), Chicago, IL, USA
- affiliation: Harvard University, Cambridge, Divsion of Engineering and Applied Sciences
- affiliation: MIT, Department of Mathematics
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2010 – today
- 2018
[c44]William M. Hoza, Adam R. Klivans:
Preserving Randomness for Adaptive Algorithms. APPROX-RANDOM 2018: 43:1-43:19- [c43]Adam R. Klivans, Pravesh K. Kothari, Raghu Meka:
Efficient Algorithms for Outlier-Robust Regression. COLT 2018: 1420-1430 - [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: 2189-2199 - [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]
- [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. Electronic Colloquium on Computational Complexity (ECCC) 23: 172 (2016) - 2014
- [j16]Prahladh Harsha, Adam R. Klivans, Raghu Meka:
Bounding the Sensitivity of Polynomial Threshold Functions. Theory of Computing 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. Electronic Colloquium on Computational Complexity (ECCC) 21: 63 (2014) - 2013
- [c35]Adam R. Klivans, Pravesh Kothari, Igor Carboni Oliveira:
Constructing Hard Functions Using Learning Algorithms. IEEE Conference on Computational Complexity 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]
- [i16]Adam R. Klivans, Raghu Meka:
Moment-Matching Polynomials. Electronic Colloquium on Computational Complexity (ECCC) 20: 8 (2013) - [i15]Adam R. Klivans, Pravesh Kothari, Igor Carboni Oliveira:
Constructing Hard Functions from Learning Algorithms. Electronic Colloquium on Computational Complexity (ECCC) 20: 129 (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. Electronic Colloquium on Computational Complexity (ECCC) 19: 127 (2012) - 2011
- [c30]Parikshit Gopalan, Adam R. Klivans, Raghu Meka, Daniel Stefankovic, Santosh 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. Electronic Colloquium on Computational Complexity (ECCC) 18: 90 (2011) - 2010
- [j14]Adam R. Klivans, Alexander A. Sherstov:
Lower Bounds for Agnostic Learning via Approximate Rank. Computational Complexity 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]Adam R. Klivans, Homin K. Lee, Andrew Wan:
Mansour's Conjecture is True for Random DNF Formulas. Electronic Colloquium on Computational Complexity (ECCC) 17: 23 (2010) - [i9]Parikshit Gopalan, Adam R. Klivans, Raghu Meka:
Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs. Electronic Colloquium on Computational Complexity (ECCC) 17: 133 (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. Journal of Machine Learning Research 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. Electronic Colloquium on Computational Complexity (ECCC) 16: 144 (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]
- 2007
- [j7]Adam R. Klivans, Alexander A. Sherstov:
Unconditional lower bounds for learning intersections of halfspaces. Machine Learning 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. Journal of Machine Learning Research 7: 587-602 (2006) - [j5]Adam R. Klivans, Amir Shpilka:
Learning Restricted Models of Arithmetic Circuits. Theory of Computing 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]
- [i5]Adam R. Klivans, Alexander A. Sherstov:
Cryptographic Hardness Results for Learning Intersections of Halfspaces. Electronic Colloquium on Computational Complexity (ECCC) 13(057) (2006) - 2005
- [c15]Lance Fortnow, Adam R. Klivans:
NP with Small Advice. IEEE Conference on Computational Complexity 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. Electronic Colloquium on Computational Complexity (ECCC)(042) (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. Electronic Colloquium on Computational Complexity (ECCC)(103) (2004) - 2003
- [j2]Adam R. Klivans, Rocco A. Servedio:
Boosting and Hard-Core Set Construction. Machine Learning 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. IEEE Conference on Computational Complexity 2002: 26 - [c7]Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio:
Learning Intersections and Thresholds of Halfspaces. FOCS 2002: 177-186 - [c6]
- 2001
- [c5]
- [c4]Adam R. Klivans, Daniel A. Spielman:
Randomness efficient identity testing of multivariate polynomials. STOC 2001: 216-223 - [c3]
1990 – 1999
- 1999
- [c2]
- [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. Electronic Colloquium on Computational Complexity (ECCC) 5(75) (1998)
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