The document discusses using xgboost for machine learning and summarizes steps to prepare data for xgboost models. It recommends binding feature data together and writing it out in the libsvm format for efficient reading into an xgboost DMatrix object. It also suggests using the data.table package to write out libsvm files in parallel for improved performance on large datasets.
Introduction of “Fairness in Learning: Classic and Contextual Bandits”Kazuto Fukuchi
1. The document discusses fairness constraints in contextual bandit problems and classic bandit problems.
2. It shows that for classic bandits, Θ(k^3) rounds are necessary and sufficient to achieve a non-trivial regret under fairness constraints.
3. For contextual bandits, it establishes a tight relationship between achieving fairness and Knows What it Knows (KWIK) learning, where KWIK learnability implies the existence of fair learning algorithms.
Conditional Image Generation with PixelCNN Decoderssuga93
The document summarizes research on conditional image generation using PixelCNN decoders. It discusses how PixelCNNs sequentially predict pixel values rather than the whole image at once. Previous work used PixelRNNs, but these were slow to train. The proposed approach uses a Gated PixelCNN that removes blind spots in the receptive field by combining horizontal and vertical feature maps. It also conditions PixelCNN layers on class labels or embeddings to generate conditional images. Experimental results show the Gated PixelCNN outperforms PixelCNN and achieves performance close to PixelRNN on CIFAR-10 and ImageNet, while training faster. It can also generate portraits conditioned on embeddings of people.
Interaction Networks for Learning about Objects, Relations and PhysicsKen Kuroki
For my presentation for a reading group. I have not in any way contributed this study, which is done by the researchers named on the first slide.
https://papers.nips.cc/paper/6418-interaction-networks-for-learning-about-objects-relations-and-physics
Introduction of "TrailBlazer" algorithmKatsuki Ohto
論文「Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning」紹介スライドです。NIPS2016読み会@PFN(2017/1/19) https://connpass.com/event/47580/ にて。
Fast and Probvably Seedings for k-MeansKimikazu Kato
The document proposes a new MCMC-based algorithm for initializing centroids in k-means clustering that does not assume a specific distribution of the input data, unlike previous work. It uses rejection sampling to emulate the distribution and select initial centroids that are widely scattered. The algorithm is proven mathematically to converge. Experimental results on synthetic and real-world datasets show it performs well with a good trade-off of accuracy and speed compared to existing techniques.
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...Shuhei Yoshida
Unsupervised learning of disentangled representations was the goal. The approach was to use GANs and maximize the mutual information between generated images and input codes. This led to the benefit of obtaining interpretable representations without supervision and at substantial additional costs.
Improving Variational Inference with Inverse Autoregressive FlowTatsuya Shirakawa
This slide was created for NIPS 2016 study meetup.
IAF and other related researches are briefly explained.
paper:
Diederik P. Kingma et al., "Improving Variational Inference with Inverse Autoregressive Flow", 2016
https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...Kenko Nakamura
This document proposes a sketch-based box-covering algorithm to efficiently analyze the fractality of massive graphs. It summarizes that some real-world networks have been found to be fractal in nature, but existing algorithms for determining fractality are too slow for large networks. The proposed method uses min-hash to represent boxes implicitly and solves the box-covering problem efficiently in the sketch space using a binary search tree and heap, allowing fractality analysis of networks with millions of edges for the first time.
Value Iteration Networks is a machine learning method for robot path planning that can operate in new environments not seen during training. It works by predicting optimal actions through learning reward values for each state and propagating rewards to determine the sum of future rewards. The method was shown to be effective for planning in grid maps and continuous control tasks, and was even applied to navigation of Wikipedia links.
Safe and Efficient Off-Policy Reinforcement Learningmooopan
This document summarizes the Retrace(λ) reinforcement learning algorithm presented by Remi Munos, Thomas Stepleton, Anna Harutyunyan and Marc G. Bellemare. Retrace(λ) is an off-policy multi-step reinforcement learning algorithm that is safe (converges for any policy), efficient (makes best use of samples when policies are close), and has lower variance than importance sampling. Empirical results on Atari 2600 games show Retrace(λ) outperforms one-step Q-learning and existing multi-step methods.
Dual Learning for Machine Translation (NIPS 2016)Toru Fujino
The paper introduces a dual learning algorithm that utilizes monolingual data to improve neural machine translation. The algorithm trains two translation models in both directions simultaneously. Experimental results show that when trained with only 10% of parallel data, the dual learning model achieves comparable results to baseline models trained on 100% of data. The dual learning mechanism also outperforms baselines when trained on full data and can help address the lack of large parallel corpora.
The document summarizes the paper "Matching Networks for One Shot Learning". It discusses one-shot learning, where a classifier can learn new concepts from only one or a few examples. It introduces matching networks, a new approach that trains an end-to-end nearest neighbor classifier for one-shot learning tasks. The matching networks architecture uses an attention mechanism to compare a test example to a small support set and achieve state-of-the-art one-shot accuracy on Omniglot and other datasets. The document provides background on one-shot learning challenges and related work on siamese networks, memory augmented neural networks, and attention mechanisms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness and well-being.
The document discusses using xgboost for machine learning and summarizes steps to prepare data for xgboost models. It recommends binding feature data together and writing it out in the libsvm format for efficient reading into an xgboost DMatrix object. It also suggests using the data.table package to write out libsvm files in parallel for improved performance on large datasets.
Introduction of “Fairness in Learning: Classic and Contextual Bandits”Kazuto Fukuchi
1. The document discusses fairness constraints in contextual bandit problems and classic bandit problems.
2. It shows that for classic bandits, Θ(k^3) rounds are necessary and sufficient to achieve a non-trivial regret under fairness constraints.
3. For contextual bandits, it establishes a tight relationship between achieving fairness and Knows What it Knows (KWIK) learning, where KWIK learnability implies the existence of fair learning algorithms.
Conditional Image Generation with PixelCNN Decoderssuga93
The document summarizes research on conditional image generation using PixelCNN decoders. It discusses how PixelCNNs sequentially predict pixel values rather than the whole image at once. Previous work used PixelRNNs, but these were slow to train. The proposed approach uses a Gated PixelCNN that removes blind spots in the receptive field by combining horizontal and vertical feature maps. It also conditions PixelCNN layers on class labels or embeddings to generate conditional images. Experimental results show the Gated PixelCNN outperforms PixelCNN and achieves performance close to PixelRNN on CIFAR-10 and ImageNet, while training faster. It can also generate portraits conditioned on embeddings of people.
Interaction Networks for Learning about Objects, Relations and PhysicsKen Kuroki
For my presentation for a reading group. I have not in any way contributed this study, which is done by the researchers named on the first slide.
https://papers.nips.cc/paper/6418-interaction-networks-for-learning-about-objects-relations-and-physics
Introduction of "TrailBlazer" algorithmKatsuki Ohto
論文「Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning」紹介スライドです。NIPS2016読み会@PFN(2017/1/19) https://connpass.com/event/47580/ にて。
Fast and Probvably Seedings for k-MeansKimikazu Kato
The document proposes a new MCMC-based algorithm for initializing centroids in k-means clustering that does not assume a specific distribution of the input data, unlike previous work. It uses rejection sampling to emulate the distribution and select initial centroids that are widely scattered. The algorithm is proven mathematically to converge. Experimental results on synthetic and real-world datasets show it performs well with a good trade-off of accuracy and speed compared to existing techniques.
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...Shuhei Yoshida
Unsupervised learning of disentangled representations was the goal. The approach was to use GANs and maximize the mutual information between generated images and input codes. This led to the benefit of obtaining interpretable representations without supervision and at substantial additional costs.
Improving Variational Inference with Inverse Autoregressive FlowTatsuya Shirakawa
This slide was created for NIPS 2016 study meetup.
IAF and other related researches are briefly explained.
paper:
Diederik P. Kingma et al., "Improving Variational Inference with Inverse Autoregressive Flow", 2016
https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...Kenko Nakamura
This document proposes a sketch-based box-covering algorithm to efficiently analyze the fractality of massive graphs. It summarizes that some real-world networks have been found to be fractal in nature, but existing algorithms for determining fractality are too slow for large networks. The proposed method uses min-hash to represent boxes implicitly and solves the box-covering problem efficiently in the sketch space using a binary search tree and heap, allowing fractality analysis of networks with millions of edges for the first time.
Value Iteration Networks is a machine learning method for robot path planning that can operate in new environments not seen during training. It works by predicting optimal actions through learning reward values for each state and propagating rewards to determine the sum of future rewards. The method was shown to be effective for planning in grid maps and continuous control tasks, and was even applied to navigation of Wikipedia links.
Safe and Efficient Off-Policy Reinforcement Learningmooopan
This document summarizes the Retrace(λ) reinforcement learning algorithm presented by Remi Munos, Thomas Stepleton, Anna Harutyunyan and Marc G. Bellemare. Retrace(λ) is an off-policy multi-step reinforcement learning algorithm that is safe (converges for any policy), efficient (makes best use of samples when policies are close), and has lower variance than importance sampling. Empirical results on Atari 2600 games show Retrace(λ) outperforms one-step Q-learning and existing multi-step methods.
Dual Learning for Machine Translation (NIPS 2016)Toru Fujino
The paper introduces a dual learning algorithm that utilizes monolingual data to improve neural machine translation. The algorithm trains two translation models in both directions simultaneously. Experimental results show that when trained with only 10% of parallel data, the dual learning model achieves comparable results to baseline models trained on 100% of data. The dual learning mechanism also outperforms baselines when trained on full data and can help address the lack of large parallel corpora.
The document summarizes the paper "Matching Networks for One Shot Learning". It discusses one-shot learning, where a classifier can learn new concepts from only one or a few examples. It introduces matching networks, a new approach that trains an end-to-end nearest neighbor classifier for one-shot learning tasks. The matching networks architecture uses an attention mechanism to compare a test example to a small support set and achieve state-of-the-art one-shot accuracy on Omniglot and other datasets. The document provides background on one-shot learning challenges and related work on siamese networks, memory augmented neural networks, and attention mechanisms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness and well-being.