Statistical Learning
Transfer Anomaly Detection by Inferring Latent Domain Representations
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
Neural Information Processing Systems http://nips.cc/
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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper the authors analyze theoretically two common graph clustering algorithms using low rank + sparsity, showing bounds on the parameter of these methods for them to work, and they present experimental validations of the results. The paper is very well written in general, although there are some minor typos. For instance, I think that the about in line 314 should be an above. Also, it seems more reasonable to me to put subsection 3.1.1
Graph Clustering With Missing Data: Convex Algorithms and Analysis
Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
Neural Information Processing Systems http://nips.cc/
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Rishi Rajalingham, Ha Hong, Najib Majaj, Elias Issa, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. Yamins, James J. DiCarlo
CORnet-S, a shallow ANN with four anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a new large-scale composite of neural and behavioral benchmarks for quantifying the functional fidelity of models of the primate ventral visual stream. Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet.
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
There are many natural models for noise.
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant
Neural Information Processing Systems http://nips.cc/
Finding the Homology of Decision Boundaries with Active Learning
In this paper, we propose an active learning algorithm to recover the homology of decision boundaries.
We thank the reviewers for their thoughtful feedback and for their appreciation of the novelty of 1 considering query-efficiency in finding homology of decision boundaries using active learning
This is an excellent point. We remark here that the same fix applied to the L ˇ C complex will help correct [3]. R3: Using topology to guide active sample acquisition. Our "model marketplace" application is different from Training classifiers with a coreset of 300 data points sampled by active learning/passive learning.