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Arsen Vasilyan

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2020 – today

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2023
[c4]
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  - conf/stoc/RubinfeldV23
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Ronitt Rubinfeld, Arsen Vasilyan:
Testing Distributional Assumptions of Learning Algorithms. STOC 2023: 1643-1656
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  - journals/corr/abs-2302-14853
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Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
An Efficient Tester-Learner for Halfspaces. CoRR abs/2302.14853 (2023)
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  - journals/corr/abs-2304-02700
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Jane Lange, Arsen Vasilyan:
Agnostic proper learning of monotone functions: beyond the black-box correction barrier. CoRR abs/2304.02700 (2023)
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  - journals/corr/abs-2305-11765
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Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Tester-Learners for Halfspaces: Universal Algorithms. CoRR abs/2305.11765 (2023)
2022
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Jane Lange, Ronitt Rubinfeld, Arsen Vasilyan:
Properly learning monotone functions via local correction. FOCS 2022: 75-86
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  - journals/corr/abs-2204-07196
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Ronitt Rubinfeld, Arsen Vasilyan:
Testing distributional assumptions of learning algorithms. CoRR abs/2204.07196 (2022)
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  - journals/corr/abs-2204-11894
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Jane Lange, Ronitt Rubinfeld, Arsen Vasilyan:
Properly learning monotone functions via local reconstruction. CoRR abs/2204.11894 (2022)
2020
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Ronitt Rubinfeld, Arsen Vasilyan:
Monotone Probability Distributions over the Boolean Cube Can Be Learned with Sublinear Samples. ITCS 2020: 28:1-28:34
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  - journals/corr/abs-2002-03415
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Ronitt Rubinfeld, Arsen Vasilyan:
Monotone probability distributions over the Boolean cube can be learned with sublinear samples. CoRR abs/2002.03415 (2020)

2010 – 2019

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2019
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Ronitt Rubinfeld, Arsen Vasilyan:
Approximating the Noise Sensitivity of a Monotone Boolean Function. APPROX-RANDOM 2019: 52:1-52:17
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  - journals/corr/abs-1904-06745
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Ronitt Rubinfeld, Arsen Vasilyan:
Approximating the noise sensitivity of a monotone Boolean function. CoRR abs/1904.06745 (2019)

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