Adam R. Klivans

Professor

Computer Science Department, University of Texas at Austin

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Research Interests:
Learning Theory, Computational Complexity, Pseudorandomness, Limit Theorems, and Gaussian Space.

Papers95 papers

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Tight Hardness Results for Training Depth-2 ReLU Networks

Surbhi Goel,Adam Klivans,Pasin Manurangsi,Daniel Reichman

ITCS, (2021)

EIBibtex

Statistical-Query Lower Bounds via Functional Gradients

Surbhi Goel,Aravind Gollakota,Adam Klivans

NIPS 2020, (2020)

Cited by3EIBibtex

Network Pruning by Greedy Subnetwork Selection

Mao Ye, Chengyue Gong, Lizhen Nie,Denny Zhou,Adam Klivans,Qiang Liu

international conference on machine learning, (2020)

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The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning

Aravind Gollakota, Sushrut Karmalkar,Adam Klivans

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Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar,Adam Klivans

ICML, pp.3587-3596, (2020)

Cited by8EIBibtex

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

Ye Mao, Gong Chengyue, Nie Lizhen,Zhou Denny,Klivans Adam,Liu Qiang

ICML, pp.10820-10830, (2020)

EIBibtex

Approximation Schemes for ReLU Regression

Diakonikolas Ilias,Goel Surbhi, Karmalkar Sushrut,Klivans Adam R.,Soltanolkotabi Mahdi

COLT, pp.1452-1485, (2020)

Cited by10EIBibtex

Learning Deep ReLU Networks Is Fixed-Parameter Tractable

Sitan Chen,Adam R. Klivans,Raghu Meka

Cited by1Bibtex

From Boltzmann Machines to Neural Networks and Back Again

Surbhi Goel,Adam Klivans,Frederic Koehler

NIPS 2020, (2020)

EIBibtex

Learning Ising Models with Independent Failures

Surbhi Goel,Daniel M. Kane,Adam R. Klivans

COLT, (2019): 1449-1469

Cited by9EIBibtex

Learning Neural Networks with Two Nonlinear Layers in Polynomial Time

Surbhi Goel,Adam R. Klivans

conference on learning theory, (2019)

Cited by54EIBibtex

List-decodeable Linear Regression

Sushrut Karmalkar,Adam R. Klivans,Pravesh K. Kothari

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019)

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List-Decodable Linear Regression

Karmalkar Sushrut,Kothari Pravesh,Klivans Adam

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Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals

Surbhi Goel, Sushrut Karmalkar,Adam Klivans

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 8582-8591

Cited by8EIBibtex

List-Decodable Linear Regression

Sushrut Karmalkar,Adam R. Klivans, Pravesh K. Kothari

NeurIPS, (2019): 7423-7432

Cited by24EIBibtex

Efficient Algorithms for Outlier-Robust Regression

Adam R. Klivans,Pravesh K. Kothari,Raghu Meka

COLT, (2018): 1420-1430

Cited by84EIBibtex

Learning One Convolutional Layer with Overlapping Patches

Surbhi Goel,Adam R. Klivans,Raghu Meka

ICML, (2018): 1778-1786

Cited by47EIBibtex

Preserving Randomness for Adaptive Algorithms

William M. Hoza,Adam R. Klivans

international workshop and international workshop on approximation randomization and combinatorial o..., (2018): 43:1-43:19

Cited by1EIBibtex

Hyperparameter Optimization: A Spectral Approach

Elad Hazan,Adam R. Klivans,Yang Yuan

international conference on learning representations, (2018)

Cited by47EIBibtex

Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time

Surbhi Goel,Adam R. Klivans

neural information processing systems, pp.2189-2199, (2017)

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Adam R. KlivansBinding

Papers95 papers

Adam R. Klivans