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1MIA-BayesBag-talk-2019
Bagged Posterior for Robust Inference
5 years ago by @kirk86
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1On the Hardness of Robust Classification
Robust Classification NeurIPS Website
5 years ago by @kirk86
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1Adversarial Robustness - Theory and Practice
Adversarial Robustness - Theory and Practice NeurIPS Chapter Tutorial
5 years ago by @kirk86
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1Dataset Shift in Machine Learning (Neural Information Processing)
http://www.acad.bg/ebook/ml/The.MIT.Press.Dataset.Shift.in.Machine.Learning.Feb.2009.eBook-DDU.pdf
5 years ago by @kirk86
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1NECSIN6
http://math.bu.edu/people/mkon/TalkNECSIN6.pdf
5 years ago by @kirk86
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machine-learning
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1Generalized Resilience and Robust Statistics
B. Zhu, J. Jiao, and J. Steinhardt. (2019)cite arxiv:1909.08755.
4 years ago by @kirk86
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generalizationout-of-distributionoutliersreadingsrobustnessstats
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1On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
T. Lin, Z. Zheng, E. Chen, M. Cuturi, and M. Jordan. (2020)cite arxiv:2006.12301Comment: Correct some typos; 46 Pages, 41 figures.
4 years ago by @kirk86
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complexitygenerative-modelsmisspecificationoptimal-transportreadingsrobustnesssampling
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3When are Non-Parametric Methods Robust?
R. Bhattacharjee, and K. Chaudhuri. (2020)cite arxiv:2003.06121Comment: under review for ICML 2020.
4 years ago by @kirk86
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2Robust Bayesian Classification Using an Optimistic Score Ratio
V. Nguyen, N. Si, and J. Blanchet. (2020)cite arxiv:2007.04458.
4 years ago by @kirk86
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1Learning under $p$-Tampering Attacks
S. Mahloujifar, D. Diochnos, and M. Mahmoody. (2017)cite arxiv:1711.03707.
4 years ago by @kirk86
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3Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
D. Diochnos, S. Mahloujifar, and M. Mahmoody. (2018)cite arxiv:1810.12272Comment: Full version of a work with the same title that will appear in NIPS 2018, 31 pages containing 5 figures, 1 table, 2 algorithms.
4 years ago by @kirk86
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2The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
S. Mahloujifar, D. Diochnos, and M. Mahmoody. (2018)cite arxiv:1809.03063.
4 years ago by @kirk86
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4Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
S. Mahloujifar, X. Zhang, M. Mahmoody, and D. Evans. (2019)cite arxiv:1905.12202Comment: 17 pages, 3 figures, 5 tables; NeurIPS final version.
4 years ago by @kirk86
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1Deterministic Variational Inference for Robust Bayesian Neural Networks
A. Wu, S. Nowozin, E. Meeds, R. Turner, J. Hernandez-Lobato, and A. Gaunt. International Conference on Learning Representations, (2019)
4 years ago by @kirk86
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2Distributionally Robust Bayesian Quadrature Optimization
T. Nguyen, S. Gupta, H. Ha, S. Rana, and S. Venkatesh. (2020)cite arxiv:2001.06814Comment: AISTATS2020.
4 years ago by @kirk86
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1Robust Sampling in Deep Learning
A. Aguilera, A. Artés-Rodríguez, and F. Pérez-Cruz. (2020)cite arxiv:2006.02734Comment: 8 pages, 3 figures.
4 years ago by @kirk86
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1Robust Regression for Safe Exploration in Control
A. Liu, G. Shi, S. Chung, A. Anandkumar, and Y. Yue. (2019)cite arxiv:1906.05819.
4 years ago by @kirk86
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2Robustness Certification of Generative Models
M. Mirman, T. Gehr, and M. Vechev. (2020)cite arxiv:2004.14756Comment: Prior version submitted to ICLR 2020.
4 years ago by @kirk86
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1Asymptotic normality of robust risk minimizers
S. Minsker. (2020)cite arxiv:2004.02328.
5 years ago by @kirk86
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2SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics
K. Kaheman, J. Kutz, and S. Brunton. (2020)cite arxiv:2004.02322Comment: 25 pages, 9 figures, 5 tables.
5 years ago by @kirk86
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2Adversarial Robustness through Regularization: A Second-Order Approach
A. Ma, F. Faghri, and A. Farahmand. (2020)cite arxiv:2004.01832.
5 years ago by @kirk86
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4The Discrete Gaussian for Differential Privacy
C. Canonne, G. Kamath, and T. Steinke. (2020)cite arxiv:2004.00010Comment: Short note, comments welcome.
5 years ago by @kirk86
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2On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design
D. Berend, A. Kontorovich, L. Reyzin, and T. Robinson. (2020)cite arxiv:2003.13561Comment: 18 pages.
5 years ago by @kirk86
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1Robust Detection of Degenerate Configurations for the Fundamental Matrix
P. Torr, A. Zisserman, and S. Maybank. Computer Vision and Image Understanding, 71 (3): 312--333 (1998)
5 years ago by @kirk86
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2Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin
C. Wei, and T. Ma. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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1MIA-BayesBag-talk-2019

1On the Hardness of Robust Classification

1Adversarial Robustness - Theory and Practice

1Dataset Shift in Machine Learning (Neural Information Processing)

1NECSIN6

publications (hide)213
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1Generalized Resilience and Robust Statistics

1On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification

3When are Non-Parametric Methods Robust?

2Robust Bayesian Classification Using an Optimistic Score Ratio

1Learning under $p$-Tampering Attacks

3Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution

2The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure

4Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

1Deterministic Variational Inference for Robust Bayesian Neural Networks

2Distributionally Robust Bayesian Quadrature Optimization

1Robust Sampling in Deep Learning

1Robust Regression for Safe Exploration in Control

2Robustness Certification of Generative Models

1Asymptotic normality of robust risk minimizers

2SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

2Adversarial Robustness through Regularization: A Second-Order Approach

4The Discrete Gaussian for Differential Privacy

2On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design

1Robust Detection of Degenerate Configurations for the Fundamental Matrix

2Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin

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