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PAC-learning in the presence of adversaries
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
Neural Information Processing Systems http://nips.cc/
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
The proper identification of anomalies can be critical for many domains.
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.
The Sparse Manifold Transform
Yubei Chen, Dylan Paiton, Bruno Olshausen
Each of these concepts have been individually explored in previous studies.
Does mitigating ML's impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, Alexandra Chouldechova
Neural Information Processing Systems http://nips.cc/
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Implicit feedback is widely used in collaborative filtering methods for recommendation.
Boosted Sparse and Low-Rank Tensor Regression
Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.
Causal Discovery from Discrete Data using Hidden Compact Representation
Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao
In fact, the additive noise model assumes that all categories of the variables are placed in the "right" order; furthermore, if
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
To mitigate the threat of adversarial examples, a large number of methods have been developed.