Statistical Learning
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction Y aodong Y u
The coding rate can be accurately computed from finite samples of degenerate subspace-like distributions and can learn intrinsic representations in supervised, self-supervised, and unsupervised settings in a unified manner.
Provable Tensor Factorization with Missing Data
Neural Information Processing Systems http://nips.cc/
Learning Hawkes Processes from a handful of events
Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
Neural Information Processing Systems http://nips.cc/
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For [23] we refer to the paper pointed by you. 5. To Reviewer_25: the overall conceptual motivation for the paper is somewhat weak... Nystrom approximation can be used to approximate the kernel matrix and speed up kernel machines, and from Table 1 we can see that the performance is suboptimal even when rank=200 (see the 5-th column). In this case, it requires 200 inner product computations to make one prediction, which is too slow for many real-time systems (e.g., web applications, robotic applications ...). Therefore state-of-the-art Nystrom method is not good enough, and we reduce the prediction time to 10~20 inner products with a better classification accuracy, which is a big improvement. Also, as we mentioned in the point 1 above, although we want to optimize the prediction time, our method still has fast training time. We agree that the psuedo landmark point technique can be potentially applied to speed up the training time, and it is an interesting research direction.
Fast Prediction for Large-Scale Kernel Machines
Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon
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. This paper focuses on l_1 regularized multi-task feature RL by means of an integration between multi-task feature learning (MTFL) and Fitted Q-learning. Clarity: The paper is mostly well written. Regarding the format of this paper, the font size is not right. A suggestion on nuclear norm: The nuclear norm is usually represented as ||\cdot||_*, where in the paper it is notated as ||\cdot||_1. There is a mistake in Assumption 5. Judging from the context, I think line 291 is right and line 299 is mistakenly written, and thus the formulation in Equation (5, 6) are wrong, where U should be U^{-1}.
