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ORCIDDaniel M. Kane
Daniel Kane 0001
Person information
- affiliation: University of California, San Diego, Department of Computer Science and Engineering, La Jolla, CA, USA
- affiliation (2011-2014): Stanford University, Department of Mathematics, Stanford, CA, USA
- affiliation (PhD 2011): Harvard University, Department of Mathematics, Cambridge, MA, USA
- affiliation (former): Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA
Other persons with the same name
- Daniel Kane 0002
![[0000-0002-0220-9962]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-0220-9962")
— Utah State University, Logan, UT, USA
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Journal Articles
- 2024
[j22]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. TheoretiCS 3 (2024)- 2022
- [j21]Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar:
vqSGD: Vector Quantized Stochastic Gradient Descent. IEEE Trans. Inf. Theory 68(7): 4573-4587 (2022) - 2021
- [j20]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustness meets algorithms. Commun. ACM 64(5): 107-115 (2021) - [j19]Daniel M. Kane, Scott Duke Kominers:
Prisoners, Rooms, and Light Switches. Electron. J. Comb. 28(1): 1 (2021) - 2020
- [j18]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. IEEE Trans. Inf. Theory 66(5): 3132-3170 (2020) - 2019
- [j17]Daniel M. Kane, Carlo Sanna, Jeffrey O. Shallit:
Waring's Theorem for Binary Powers. Comb. 39(6): 1335-1350 (2019) - [j16]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal Linear Decision Trees for k-SUM and Related Problems. J. ACM 66(3): 16:1-16:18 (2019) - [j15]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. 48(2): 742-864 (2019) - [j14]Daniel Kane, Shachar Lovett, Sankeerth Rao:
The Independence Number of the Birkhoff Polytope Graph, and Applications to Maximally Recoverable Codes. SIAM J. Comput. 48(4): 1425-1435 (2019) - [j13]Ilgweon Kang, Fang Qiao, Dongwon Park, Daniel Kane, Evangeline F. Y. Young, Chung-Kuan Cheng, Ronald L. Graham:
Three-dimensional Floorplan Representations by Using Corner Links and Partial Order. ACM Trans. Design Autom. Electr. Syst. 24(1): 13:1-13:33 (2019) - 2018
- [j12]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the Discrete Fourier Transform. SIAM J. Comput. 47(6): 2451-2487 (2018) - 2017
- [j11]Daniel Kane, Terence Tao:
A Bound on Partitioning Clusters. Electron. J. Comb. 24(2): 2 (2017) - 2016
- [j10]Daniel Kane, Osamu Watanabe:
A Short Implicant of a CNF Formula with Many Satisfying Assignments. Algorithmica 76(4): 1203-1223 (2016) - 2015
- [j9]Bobbie Chern, Persi Diaconis, Daniel M. Kane, Robert C. Rhoades:
Central limit theorems for some set partition statistics. Adv. Appl. Math. 70: 92-105 (2015) - 2014
- [j8]Daniel M. Kane:
The correct exponent for the Gotsman-Linial Conjecture. Comput. Complex. 23(2): 151-175 (2014) - [j7]Daniel M. Kane, Jelani Nelson:
Sparser Johnson-Lindenstrauss Transforms. J. ACM 61(1): 4:1-4:23 (2014) - 2013
- [j6]Daniel M. Kane:
A Monotone Function Given By a Low-Depth Decision Tree That Is Not an Approximate Junta. Theory Comput. 9: 587-592 (2013) - 2011
- [j5]Daniel M. Kane:
The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions. Comput. Complex. 20(2): 389-412 (2011) - [j4]Daniel M. Kane, Samuel A. Kutin:
Quantum interpolation of polynomials. Quantum Inf. Comput. 11(1&2): 95-103 (2011) - 2010
- [j3]Daniel M. Kane:
On Solving Games Constructed Using Both Short and Long Conjunctive Sums. Integers 10: G4 (2010) - 2009
- [j2]Timothy G. Abbott, Michael A. Burr, Timothy M. Chan, Erik D. Demaine, Martin L. Demaine, John Hugg, Daniel Kane, Stefan Langerman, Jelani Nelson, Eynat Rafalin, Kathryn Seyboth, Vincent Yeung:
Dynamic ham-sandwich cuts in the plane. Comput. Geom. 42(5): 419-428 (2009) - 2008
- [j1]Daniel Kane, Steven Sivek:
On the Sn-Modules Generated by Partitions of a Given Shape. Electron. J. Comb. 15(1) (2008)
Conference and Workshop Papers
- 2024
- [c125]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. SODA 2024: 3197-3235 - 2023
- [c124]Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT 2023: 1588-1617 - [c123]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. COLT 2023: 2211-2239 - [c122]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. COLT 2023: 2319-2349 - [c121]Daniel Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. COLT 2023: 3014-3028 - [c120]Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang:
Statistical and Computational Limits for Tensor-on-Tensor Association Detection. COLT 2023: 5260-5310 - [c119]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. ICML 2023: 7886-7921 - [c118]Ilias Diakonikolas, Daniel Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. ICML 2023: 7922-7938 - [c117]Ilias Diakonikolas, Jelena Diakonikolas, Daniel Kane, Puqian Wang, Nikos Zarifis:
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. NeurIPS 2023 - [c116]Ilias Diakonikolas, Daniel Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. NeurIPS 2023 - [c115]Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas:
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. NeurIPS 2023 - [c114]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. NeurIPS 2023 - [c113]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. NeurIPS 2023 - [c112]Ilias Diakonikolas, Daniel Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. NeurIPS 2023 - [c111]Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett:
Sampling Equilibria: Fast No-Regret Learning in Structured Games. SODA 2023: 3817-3855 - [c110]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. SOSA 2023: 348-352 - [c109]Ilias Diakonikolas, Christos Tzamos, Daniel M. Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and Its Application to Halfspace Learning. STOC 2023: 1741-1754 - 2022
- [c108]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Hardness of Learning a Single Neuron with Adversarial Label Noise. AISTATS 2022: 8199-8213 - [c107]Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. AISTATS 2022: 10622-10639 - [c106]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gap in Reinforcement Learning. COLT 2022: 1282-1302 - [c105]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. COLT 2022: 3015-3069 - [c104]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. COLT 2022: 3936-3978 - [c103]Ilias Diakonikolas, Daniel Kane:
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. COLT 2022: 4258-4282 - [c102]Ilias Diakonikolas, Daniel Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. COLT 2022: 4535-4547 - [c101]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. COLT 2022: 4703-4763 - [c100]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. ICML 2022: 5061-5117 - [c99]Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS 2022 - [c98]Clément L. Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu:
Nearly-Tight Bounds for Testing Histogram Distributions. NeurIPS 2022 - [c97]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. NeurIPS 2022 - [c96]Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. NeurIPS 2022 - [c95]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. NeurIPS 2022 - [c94]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. NeurIPS 2022 - [c93]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning general halfspaces with general Massart noise under the Gaussian distribution. STOC 2022: 874-885 - [c92]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly learning mixtures of k arbitrary Gaussians. STOC 2022: 1234-1247 - [c91]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering mixture models in almost-linear time via list-decodable mean estimation. STOC 2022: 1262-1275 - 2021
- [c90]Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar:
vqSGD: Vector Quantized Stochastic Gradient Descent. AISTATS 2021: 2197-2205 - [c89]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. COLT 2021: 1511-1521 - [c88]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. COLT 2021: 1522-1551 - [c87]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model. COLT 2021: 1552-1584 - [c86]Ilias Diakonikolas, Russell Impagliazzo, Daniel M. Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. COLT 2021: 1585-1644 - [c85]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. COLT 2021: 1645-1682 - [c84]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. COLT 2021: 2358-2387 - [c83]Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran:
The Entropy of Lies: Playing Twenty Questions with a Liar. ITCS 2021: 1:1-1:16 - [c82]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. NeurIPS 2021: 3191-3204 - [c81]Ilias Diakonikolas, Daniel Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. NeurIPS 2021: 7732-7744 - [c80]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. NeurIPS 2021: 10195-10208 - [c79]Daniel M. Kane:
Robust Learning of Mixtures of Gaussians. SODA 2021: 1246-1258 - [c78]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Efficiently learning halfspaces with Tsybakov noise. STOC 2021: 88-101 - [c77]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal testing of discrete distributions with high probability. STOC 2021: 542-555 - 2020
- [c76]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis:
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. COLT 2020: 1514-1539 - [c75]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT 2020: 1957-2006 - [c74]Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari:
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. FOCS 2020: 149-159 - [c73]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. FOCS 2020: 184-195 - [c72]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS 2020: 1034-1044 - [c71]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. NeurIPS 2020 - [c70]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. NeurIPS 2020 - [c69]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. NeurIPS 2020 - [c68]Ilias Diakonikolas, Daniel Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. NeurIPS 2020 - [c67]Max Hopkins, Daniel Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. NeurIPS 2020 - 2019
- [c66]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. COLT 2019: 318-341 - [c65]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao:
Communication and Memory Efficient Testing of Discrete Distributions. COLT 2019: 1070-1106 - [c64]Ilias Diakonikolas, Daniel M. Kane, John Peebles:
Testing Identity of Multidimensional Histograms. COLT 2019: 1107-1131 - [c63]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. COLT 2019: 1449-1469 - [c62]Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. COLT 2019: 1903-1943 - [c61]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML 2019: 1596-1606 - [c60]Daniel M. Kane, Richard Ryan Williams:
The Orthogonal Vectors Conjecture for Branching Programs and Formulas. ITCS 2019: 48:1-48:15 - [c59]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. NeurIPS 2019: 10473-10484 - [c58]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. NeurIPS 2019: 10688-10699 - [c57]Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld:
Private Testing of Distributions via Sample Permutations. NeurIPS 2019: 10877-10888 - [c56]Ilias Diakonikolas, Daniel M. Kane:
Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). STOC 2019: 804-815 - 2018
- [c55]Daniel Kane, Sankeerth Rao:
A PRG for Boolean PTF of Degree 2 with Seed Length Subpolynomial in epsilon and Logarithmic in n. CCC 2018: 2:1-2:24 - [c54]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized Comparison Trees for Point-Location Problems. ICALP 2018: 82:1-82:13 - [c53]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Conditional Independence of Discrete Distributions. ITA 2018: 1-57 - [c52]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. NeurIPS 2018: 6204-6213 - [c51]Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart:
Robust Learning of Fixed-Structure Bayesian Networks. NeurIPS 2018: 10304-10316 - [c50]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA 2018: 2683-2702 - [c49]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. STOC 2018: 554-563 - [c48]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing conditional independence of discrete distributions. STOC 2018: 735-748 - [c47]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
List-decodable robust mean estimation and learning mixtures of spherical gaussians. STOC 2018: 1047-1060 - [c46]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning geometric concepts with nasty noise. STOC 2018: 1061-1073 - 2017
- [c45]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. COLT 2017: 370-448 - [c44]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Multivariate Log-concave Distributions. COLT 2017: 711-727 - [c43]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures. FOCS 2017: 73-84 - [c42]Daniel Kane, Shachar Lovett, Sankeerth Rao:
The Independence Number of the Birkhoff Polytope Graph, and Applications to Maximally Recoverable Codes. FOCS 2017: 252-259 - [c41]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active Classification with Comparison Queries. FOCS 2017: 355-366 - [c40]Daniel Kane, Sushrut Karmalkar, Eric Price:
Robust Polynomial Regression up to the Information Theoretic Limit. FOCS 2017: 391-402 - [c39]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Near-Optimal Closeness Testing of Discrete Histogram Distributions. ICALP 2017: 8:1-8:15 - [c38]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. ICML 2017: 999-1008 - [c37]Valentine Kabanets, Daniel M. Kane, Zhenjian Lu:
A polynomial restriction lemma with applications. STOC 2017: 615-628 - 2016
- [c36]Fang Qiao, Ilgweon Kang, Daniel Kane, Fung Yu Young, Chung-Kuan Cheng, Ronald L. Graham:
3D floorplan representations: Corner links and partial order. 3DIC 2016: 1-5 - [c35]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables. COLT 2016: 831-849 - [c34]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. COLT 2016: 850-878 - [c33]Mihir Bellare, Daniel Kane, Phillip Rogaway:
Big-Key Symmetric Encryption: Resisting Key Exfiltration. CRYPTO (1) 2016: 373-402 - [c32]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. FOCS 2016: 655-664 - [c31]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. FOCS 2016: 685-694 - [c30]Xue Chen, Daniel M. Kane, Eric Price, Zhao Song:
Fourier-Sparse Interpolation without a Frequency Gap. FOCS 2016: 741-750 - [c29]Daniel M. Kane, Ryan Williams:
Super-linear gate and super-quadratic wire lower bounds for depth-two and depth-three threshold circuits. STOC 2016: 633-643 - [c28]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
The fourier transform of poisson multinomial distributions and its algorithmic applications. STOC 2016: 1060-1073 - 2015
- [c27]Mihir Bellare, Joseph Jaeger, Daniel Kane:
Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks. CCS 2015: 1431-1440 - [c26]Daniel M. Kane:
A Polylogarithmic PRG for Degree 2 Threshold Functions in the Gaussian Setting. CCC 2015: 567-581 - [c25]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the Discrete Fourier Transform. FOCS 2015: 903-922 - [c24]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. FOCS 2015: 1183-1202 - [c23]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Testing Identity of Structured Distributions. SODA 2015: 1841-1854 - 2014
- [c22]Daniel M. Kane:
A Pseudorandom Generator for Polynomial Threshold Functions of Gaussian with Subpolynomial Seed Length. CCC 2014: 217-228 - [c21]Daniel M. Kane, Osamu Watanabe:
A Short Implicant of a CNF Formula with Many Satisfying Assignments. ISAAC 2014: 273-284 - [c20]Daniel M. Kane:
The average sensitivity of an intersection of half spaces. STOC 2014: 437-440 - 2013
- [c19]Daniel M. Kane:
The Correct Exponent for the Gotsman-Linial Conjecture. CCC 2013: 56-64 - [c18]Daniel M. Kane, Adam R. Klivans, Raghu Meka:
Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. COLT 2013: 522-545 - [c17]Daniel M. Kane, Raghu Meka:
A PRG for lipschitz functions of polynomials with applications to sparsest cut. STOC 2013: 1-10 - 2012
- [c16]Eric Blais, Daniel M. Kane:
Tight Bounds for Testing k-Linearity. APPROX-RANDOM 2012: 435-446 - [c15]Daniel M. Kane:
A Structure Theorem for Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions. FOCS 2012: 91-100 - [c14]Daniel M. Kane, Kurt Mehlhorn, Thomas Sauerwald, He Sun:
Counting Arbitrary Subgraphs in Data Streams. ICALP (2) 2012: 598-609 - [c13]Daniel M. Kane, Jelani Nelson:
Sparser Johnson-Lindenstrauss transforms. SODA 2012: 1195-1206 - 2011
- [c12]Daniel Kane, Raghu Meka, Jelani Nelson:
Almost Optimal Explicit Johnson-Lindenstrauss Families. APPROX-RANDOM 2011: 628-639 - [c11]Daniel M. Kane:
k-Independent Gaussians Fool Polynomial Threshold Functions. CCC 2011: 252-261 - [c10]Daniel M. Kane:
A Small PRG for Polynomial Threshold Functions of Gaussians. FOCS 2011: 257-266 - [c9]Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff:
Fast moment estimation in data streams in optimal space. STOC 2011: 745-754 - 2010
- [c8]Daniel M. Kane:
The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions. CCC 2010: 205-210 - [c7]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. FOCS 2010: 11-20 - [c6]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
An optimal algorithm for the distinct elements problem. PODS 2010: 41-52 - [c5]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
On the Exact Space Complexity of Sketching and Streaming Small Norms. SODA 2010: 1161-1178 - 2009
- [c4]Erik D. Demaine, Dion Harmon, John Iacono, Daniel Kane, Mihai Patrascu:
The geometry of binary search trees. SODA 2009: 496-505 - [c3]Daniel Kane, Gregory N. Price, Erik D. Demaine:
A Pseudopolynomial Algorithm for Alexandrov's Theorem. WADS 2009: 435-446 - 2005
- [c2]Timothy G. Abbott, Erik D. Demaine, Martin L. Demaine, Daniel Kane, Stefan Langerman, Jelani Nelson, Vincent Yeung:
Dynamic Ham-Sandwich Cuts of Convex Polygons in the Plane. CCCG 2005: 61-64 - [c1]Timothy G. Abbott, Daniel Kane, Paul Valiant:
On the Complexity of Two-PlayerWin-Lose Games. FOCS 2005: 113-122
Parts in Books or Collections
- 2020
- [p1]Ilias Diakonikolas, Daniel M. Kane:
Robust High-Dimensional Statistics. Beyond the Worst-Case Analysis of Algorithms 2020: 382-402
Informal and Other Publications
- 2024
- [i149]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locality Bounds for Sampling Hamming Slices. CoRR abs/2402.14278 (2024) - [i148]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. CoRR abs/2403.02300 (2024) - [i147]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. CoRR abs/2403.04744 (2024) - [i146]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. CoRR abs/2403.10416 (2024) - [i145]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locality Bounds for Sampling Hamming Slices. Electron. Colloquium Comput. Complex. TR24: TR24-031 (2024) - 2023
- [i144]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? CoRR abs/2302.06285 (2023) - [i143]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. CoRR abs/2302.06512 (2023) - [i142]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR abs/2302.12940 (2023) - [i141]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. CoRR abs/2303.05485 (2023) - [i140]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas:
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. CoRR abs/2305.00966 (2023) - [i139]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. CoRR abs/2305.02544 (2023) - [i138]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Bounded Covariance GMMs. CoRR abs/2306.13057 (2023) - [i137]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. CoRR abs/2306.16352 (2023) - [i136]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. CoRR abs/2307.08438 (2023) - [i135]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials. CoRR abs/2307.12840 (2023) - [i134]Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng:
New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions. CoRR abs/2308.00089 (2023) - [i133]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. CoRR abs/2310.11876 (2023) - [i132]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. CoRR abs/2310.15932 (2023) - [i131]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Testing Closeness of Multivariate Distributions via Ramsey Theory. CoRR abs/2311.13154 (2023) - [i130]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. CoRR abs/2312.01547 (2023) - [i129]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas:
Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation. CoRR abs/2312.11769 (2023) - [i128]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostically Learning Multi-index Models with Queries. CoRR abs/2312.16616 (2023) - 2022
- [i127]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gaps in Reinforcement Learning. CoRR abs/2202.05444 (2022) - [i126]Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. CoRR abs/2203.03009 (2022) - [i125]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. CoRR abs/2204.12399 (2022) - [i124]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. CoRR abs/2206.03441 (2022) - [i123]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. CoRR abs/2206.04589 (2022) - [i122]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. CoRR abs/2206.05245 (2022) - [i121]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Near-Optimal Bounds for Testing Histogram Distributions. CoRR abs/2207.06596 (2022) - [i120]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2207.14266 (2022) - [i119]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. CoRR abs/2210.09949 (2022) - [i118]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. CoRR abs/2210.13706 (2022) - [i117]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. CoRR abs/2211.16333 (2022) - [i116]Ilias Diakonikolas, Christos Tzamos, Daniel M. Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. CoRR abs/2212.03008 (2022) - [i115]Daniel M. Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. CoRR abs/2212.11221 (2022) - [i114]Ilias Diakonikolas, Christos Tzamos, Daniel Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [i113]Daniel Kane, Andreas Fackler, Adam Gagol, Damian Straszak:
Highway: Efficient Consensus with Flexible Finality. CoRR abs/2101.02159 (2021) - [i112]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. CoRR abs/2102.02171 (2021) - [i111]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals. CoRR abs/2102.04401 (2021) - [i110]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. CoRR abs/2102.05047 (2021) - [i109]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. CoRR abs/2102.05629 (2021) - [i108]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan, Daniel Kane:
Fooling Gaussian PTFs via Local Hyperconcentration. CoRR abs/2103.07809 (2021) - [i107]Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. CoRR abs/2106.07779 (2021) - [i106]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation. CoRR abs/2106.08537 (2021) - [i105]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. CoRR abs/2106.09689 (2021) - [i104]Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. CoRR abs/2107.05582 (2021) - [i103]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Threshold Phenomena in Learning Halfspaces with Massart Noise. CoRR abs/2108.08767 (2021) - [i102]Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. CoRR abs/2109.11515 (2021) - [i101]Daniel M. Kane, Shahed Sharif, Alice Silverberg:
Quantum Money from Quaternion Algebras. CoRR abs/2109.12643 (2021) - [i100]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. CoRR abs/2111.04746 (2021) - [i99]Ilias Diakonikolas, Daniel M. Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. CoRR abs/2112.09104 (2021) - [i98]Daniel M. Kane, Shahed Sharif, Alice Silverberg:
Quantum Money from Quaternion Algebras. IACR Cryptol. ePrint Arch. 2021: 1294 (2021) - 2020
- [i97]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR abs/2001.05497 (2020) - [i96]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR abs/2004.11380 (2020) - [i95]Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar:
Robustly Learning any Clusterable Mixture of Gaussians. CoRR abs/2005.06417 (2020) - [i94]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. CoRR abs/2006.10715 (2020) - [i93]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis:
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. CoRR abs/2006.12476 (2020) - [i92]Ilias Diakonikolas, Daniel M. Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. CoRR abs/2006.16200 (2020) - [i91]Daniel M. Kane:
Robust Learning of Mixtures of Gaussians. CoRR abs/2007.05912 (2020) - [i90]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. CoRR abs/2007.15220 (2020) - [i89]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. CoRR abs/2007.15618 (2020) - [i88]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal Testing of Discrete Distributions with High Probability. CoRR abs/2009.06540 (2020) - [i87]Daniel M. Kane, Scott Duke Kominers:
Prisoners, Rooms, and Lightswitches. CoRR abs/2009.08575 (2020) - [i86]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. CoRR abs/2010.01705 (2020) - [i85]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. CoRR abs/2011.09973 (2020) - [i84]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly Learning Mixtures of k Arbitrary Gaussians. CoRR abs/2012.02119 (2020) - [i83]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. CoRR abs/2012.07774 (2020) - [i82]Ilias Diakonikolas, Daniel M. Kane:
Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2012.09720 (2020) - [i81]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. CoRR abs/2012.15802 (2020) - [i80]Ilias Diakonikolas, Themis Gouleakis, Daniel Kane, John Peebles, Eric Price:
Optimal Testing of Discrete Distributions with High Probability. Electron. Colloquium Comput. Complex. TR20 (2020) - 2019
- [i79]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. CoRR abs/1902.04728 (2019) - [i78]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. CoRR abs/1902.05876 (2019) - [i77]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao:
Communication and Memory Efficient Testing of Discrete Distributions. CoRR abs/1906.04709 (2019) - [i76]Max Hopkins, Daniel M. Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. CoRR abs/1907.03816 (2019) - [i75]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. CoRR abs/1908.11335 (2019) - [i74]Ilias Diakonikolas, Daniel M. Kane:
Recent Advances in Algorithmic High-Dimensional Robust Statistics. CoRR abs/1911.05911 (2019) - [i73]Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. CoRR abs/1911.08085 (2019) - 2018
- [i72]Daniel M. Kane, Carlo Sanna, Jeffrey O. Shallit:
Waring's Theorem for Binary Powers. CoRR abs/1801.04483 (2018) - [i71]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR abs/1803.02815 (2018) - [i70]Ilias Diakonikolas, Daniel M. Kane, John Peebles:
Testing Identity of Multidimensional Histograms. CoRR abs/1804.03636 (2018) - [i69]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized comparison trees for point-location problems. CoRR abs/1804.08237 (2018) - [i68]Daniel M. Kane:
Quantum Money from Modular Forms. CoRR abs/1809.05925 (2018) - [i67]Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran:
The entropy of lies: playing twenty questions with a liar. CoRR abs/1811.02177 (2018) - [i66]Ilias Diakonikolas, Daniel M. Kane:
Degree-d Chow Parameters Robustly Determine Degree-d PTFs (and Algorithmic Applications). CoRR abs/1811.03491 (2018) - [i65]Ilias Diakonikolas, Daniel Kane:
Degree-$d$ Chow Parameters Robustly Determine Degree-$d$ PTFs (and Algorithmic Applications). Electron. Colloquium Comput. Complex. TR18 (2018) - [i64]Daniel M. Kane, Shachar Lovett, Shay Moran:
Generalized comparison trees for point-location problems. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [i63]Daniel M. Kane, Shachar Lovett, Sankeerth Rao:
Labeling the complete bipartite graph with no zero cycles. CoRR abs/1702.05773 (2017) - [i62]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. CoRR abs/1703.00893 (2017) - [i61]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Near-Optimal Closeness Testing of Discrete Histogram Distributions. CoRR abs/1703.01913 (2017) - [i60]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active classification with comparison queries. CoRR abs/1704.03564 (2017) - [i59]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. CoRR abs/1704.03866 (2017) - [i58]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. CoRR abs/1705.01720 (2017) - [i57]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Geometric Concepts with Nasty Noise. CoRR abs/1707.01242 (2017) - [i56]Daniel M. Kane, Sushrut Karmalkar, Eric Price:
Robust polynomial regression up to the information theoretic limit. CoRR abs/1708.03257 (2017) - [i55]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. CoRR abs/1709.02087 (2017) - [i54]Daniel M. Kane, R. Ryan Williams:
The Orthogonal Vectors Conjecture for Branching Programs and Formulas. CoRR abs/1709.05294 (2017) - [i53]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. CoRR abs/1711.05893 (2017) - [i52]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians. CoRR abs/1711.07211 (2017) - [i51]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Conditional Independence of Discrete Distributions. CoRR abs/1711.11560 (2017) - [i50]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. Electron. Colloquium Comput. Complex. TR17 (2017) - [i49]Valentine Kabanets, Daniel M. Kane, Zhenjian Lu:
A Polynomial Restriction Lemma with Applications. Electron. Colloquium Comput. Complex. TR17 (2017) - [i48]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal linear decision trees for k-SUM and related problems. Electron. Colloquium Comput. Complex. TR17 (2017) - [i47]Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. Electron. Colloquium Comput. Complex. TR17 (2017) - [i46]Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang:
Active classification with comparison queries. Electron. Colloquium Comput. Complex. TR17 (2017) - [i45]Daniel M. Kane, Shachar Lovett, Sankeerth Rao:
Labeling the complete bipartite graph with no zero cycles. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [i44]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. CoRR abs/1601.05557 (2016) - [i43]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Zheng Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. CoRR abs/1604.06443 (2016) - [i42]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Multivariate Log-concave Distributions. CoRR abs/1605.08188 (2016) - [i41]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Efficient Robust Proper Learning of Log-concave Distributions. CoRR abs/1606.03077 (2016) - [i40]Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Robust Learning of Fixed-Structure Bayesian Networks. CoRR abs/1606.07384 (2016) - [i39]Xue Chen, Daniel M. Kane, Eric Price, Zhao Song:
Fourier-sparse interpolation without a frequency gap. CoRR abs/1609.01361 (2016) - [i38]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. CoRR abs/1611.03473 (2016) - [i37]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. CoRR abs/1612.03156 (2016) - [i36]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. Electron. Colloquium Comput. Complex. TR16 (2016) - [i35]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. Electron. Colloquium Comput. Complex. TR16 (2016) - [i34]Mihir Bellare, Daniel Kane, Phillip Rogaway:
Big-Key Symmetric Encryption: Resisting Key Exfiltration. IACR Cryptol. ePrint Arch. 2016: 541 (2016) - 2015
- [i33]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables. CoRR abs/1505.00662 (2015) - [i32]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness via the discrete Fourier transform. CoRR abs/1506.04350 (2015) - [i31]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. CoRR abs/1508.05538 (2015) - [i30]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications. CoRR abs/1511.03592 (2015) - [i29]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. CoRR abs/1511.04066 (2015) - [i28]Daniel M. Kane, Ryan Williams:
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits. CoRR abs/1511.07860 (2015) - [i27]Daniel M. Kane, Ryan Williams:
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits. Electron. Colloquium Comput. Complex. TR15 (2015) - [i26]Mihir Bellare, Joseph Jaeger, Daniel Kane:
Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks. IACR Cryptol. ePrint Arch. 2015: 808 (2015) - 2014
- [i25]Daniel M. Kane:
A Polylogarithmic PRG for Degree $2$ Threshold Functions in the Gaussian Setting. CoRR abs/1404.1103 (2014) - [i24]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Testing Identity of Structured Distributions. CoRR abs/1410.2266 (2014) - [i23]Parikshit Gopalan, Daniel M. Kane, Raghu Meka:
Pseudorandomness for concentration bounds and signed majorities. CoRR abs/1411.4584 (2014) - 2013
- [i22]Daniel M. Kane:
The Average Sensitivity of an Intersection of Half Spaces. CoRR abs/1309.2987 (2013) - [i21]Daniel M. Kane, Osamu Watanabe:
A Short Implicant of CNFs with Relatively Many Satisfying Assignments. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [i20]Daniel M. Kane:
A Structure Theorem for Poorly Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions. CoRR abs/1204.0543 (2012) - [i19]Daniel M. Kane:
A Low-Depth Monotone Function that is not an Approximate Junta. CoRR abs/1206.6541 (2012) - [i18]Daniel M. Kane:
A Pseudorandom Generator for Polynomial Threshold Functions of Gaussian with Subpolynomial Seed Length. CoRR abs/1210.1280 (2012) - [i17]Daniel M. Kane:
The Correct Exponent for the Gotsman-Linial Conjecture. CoRR abs/1210.1283 (2012) - [i16]Daniel M. Kane, Raghu Meka:
A PRG for Lipschitz Functions of Polynomials with Applications to Sparsest Cut. CoRR abs/1211.1109 (2012) - 2011
- [i15]Daniel M. Kane:
A Small PRG for Polynomial Threshold Functions of Gaussians. CoRR abs/1104.1209 (2011) - 2010
- [i14]Daniel M. Kane, Jelani Nelson:
A Derandomized Sparse Johnson-Lindenstrauss Transform. CoRR abs/1006.3585 (2010) - [i13]Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff:
Fast Moment Estimation in Data Streams in Optimal Space. CoRR abs/1007.4191 (2010) - [i12]Daniel M. Kane:
Unary Subset-Sum is in Logspace. CoRR abs/1012.1336 (2010) - [i11]Daniel M. Kane, Jelani Nelson:
A Sparser Johnson-Lindenstrauss Transform. CoRR abs/1012.1577 (2010) - [i10]Daniel M. Kane:
k-Independent Gaussians Fool Polynomial Threshold Functions. CoRR abs/1012.1614 (2010) - [i9]Daniel Kane, Raghu Meka, Jelani Nelson:
Almost Optimal Explicit Johnson-Lindenstrauss Transformations. Electron. Colloquium Comput. Complex. TR10 (2010) - [i8]Daniel M. Kane, Jelani Nelson:
A Derandomized Sparse Johnson-Lindenstrauss Transform. Electron. Colloquium Comput. Complex. TR10 (2010) - 2009
- [i7]Daniel Kane, Gregory Nathan Price, Erik D. Demaine:
A Pseudopolynomial Algorithm for Alexandrov's Theorem. Computational Geometry 2009 - [i6]Daniel M. Kane, Samuel A. Kutin:
Quantum interpolation of polynomials. CoRR abs/0909.5683 (2009) - [i5]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. CoRR abs/0911.3389 (2009) - [i4]Daniel M. Kane:
The Gaussian Surface Area and Noise Sensitivity of Degree-$d$ Polynomials. CoRR abs/0912.2709 (2009) - [i3]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [i2]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
Revisiting Norm Estimation in Data Streams. CoRR abs/0811.3648 (2008) - [i1]Daniel Kane, Gregory N. Price, Erik D. Demaine:
A Pseudopolynomial Algorithm for Alexandrov's Theorem. CoRR abs/0812.5030 (2008)
Coauthor Index
[j20] [j18] [j15] [c125] [c123] [c122] [c121] [c120] [c119] [c118] [c117] [c116] [c115] [c114] [c113] [c112] [c110] [c109] [c108] [c104] [c103] [c102] [c101] [c100] [c99] [c98] [c97] [c96] [c95] [c94] [c93] [c92] [c91] [c89] [c88] [c87] [c86] [c85] [c82] [c81] [c80] [c78] [c77] [c76] [c74] [c73] [c71] [c70] [c69] [c68] [c65] [c64] [c61] [c59] [c58] [c57] [c56] [c53] [c52] [c51] [c50] [c48] [c47] [c46] [c45] [c44] [c43] [c39] [c38] [c35] [c34] [c32] [c31] [c28] [c24] [c23] [c7] [p1] [i148] [i147] [i146] [i143] [i141] [i140] [i139] [i138] [i137] [i136] [i135] [i133] [i132] [i131] [i130] [i129] [i128] [i125] [i124] [i123] [i122] [i121] [i120] [i119] [i118] [i117] [i116] [i115] [i114] [i112] [i111] [i109] [i107] [i106] [i105] [i104] [i103] [i102] [i99] [i95] [i94] [i93] [i92] [i90] [i89] [i88] [i86] [i85] [i84] [i83] [i82] [i81] [i80] [i77] [i75] [i74] [i73] [i71] [i70] [i66] [i65] [i62] [i61] [i59] [i57] [i55] [i52] [i51] [i50] [i44] [i43] [i42] [i41] [i40] [i38] [i37] [i36] [i35] [i33] [i31] [i30] [i29] [i24] [i5] [i3]
Jerry Li 0001
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aka: Jerry Zheng Li
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