Issei Sato

is this you? claim profile

∙ 0 followers
The University of Tokyo

Lecture, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo since 2015, Affiliated Lecture, Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo since 2015, Affiliated Lecture, Department of Information Science, Faculty of Science, The University of Tokyo since 2015, Assistant professor, Academic Information Science Research Division Information Technology Center of University of Tokyo from 2011-2015, My Ph.D. advisor was Hiroshi Nakagawa at Mathematical Informatics, Graduate School of Information Science and Technology in University of Tokyo from 2008-2011, Japan Society for the Promotion of Science Research Fellow (DC1) from 2008-2011.

Featured Co-authors

Dacheng Tao
256 publications
Masashi Sugiyama
157 publications
Gang Niu
54 publications
Yutaka Matsuo
27 publications
Junya Honda
19 publications
Xi Yang
18 publications
Masatoshi Uehara
17 publications
Huishuai Zhang
16 publications
Nontawat Charoenphakdee
15 publications
Han Bao
14 publications
Miao Xu
12 publications

Stable Weight Decay Regularization

Weight decay is a popular regularization technique for training of deep ...
11/23/2020 ∙ by Zeke Xie, et al. ∙ 9 ∙ share

read it
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues which rarely exi...
11/12/2020 ∙ by Zeke Xie, et al. ∙ 1 ∙ share

read it
Classification from Ambiguity Comparisons

Labeling data is an unavoidable pre-processing procedure for most machin...
08/03/2020 ∙ by Zhenghang Cui, et al. ∙ 8 ∙ share

read it
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain

Label disagreement between human experts is a common issue in the medica...
07/03/2020 ∙ by Takahiro Mimori, et al. ∙ 67 ∙ share

read it
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia

Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Ra...
06/29/2020 ∙ by Zeke Xie, et al. ∙ 8 ∙ share

read it
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning

The prototypical network (ProtoNet) is a few-shot learning framework tha...
06/15/2020 ∙ by Kei Mukaiyama, et al. ∙ 0 ∙ share

read it
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator

Making a reliable inference in complex models is an essential issue in s...
06/13/2020 ∙ by Masahiro Fujisawa, et al. ∙ 0 ∙ share

read it
Similarity-based Classification: Connecting Similarity Learning to Binary Classification

In real-world classification problems, pairwise supervision (i.e., a pai...
06/11/2020 ∙ by Han Bao, et al. ∙ 6 ∙ share

read it
Sequential Gallery for Interactive Visual Design Optimization

Visual design tasks often involve tuning many design parameters. For exa...
05/08/2020 ∙ by Yuki Koyama, et al. ∙ 0 ∙ share

read it
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time

The Gaussian process bandit is a problem in which we want to find a maxi...
03/10/2020 ∙ by Hideaki Imamura, et al. ∙ 9 ∙ share

read it
Few-shot Domain Adaptation by Causal Mechanism Transfer

We study few-shot supervised domain adaptation (DA) for regression probl...
02/10/2020 ∙ by Takeshi Teshima, et al. ∙ 3 ∙ share

read it
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast

Stochastic optimization algorithms, such as Stochastic Gradient Descent ...
02/10/2020 ∙ by Zeke Xie, et al. ∙ 14 ∙ share

read it
Bayesian interpretation of SGD as Ito process

The current interpretation of stochastic gradient descent (SGD) as a sto...
11/20/2019 ∙ by Soma Yokoi, et al. ∙ 20 ∙ share

read it
Classification from Triplet Comparison Data

Learning from triplet comparison data has been extensively studied in th...
07/24/2019 ∙ by Zhenghang Cui, et al. ∙ 1 ∙ share

read it
Interactive Subspace Exploration on Generative Image Modelling

Generative image modeling techniques such as GAN demonstrate highly conv...
06/24/2019 ∙ by Toby Chong Long Hin, et al. ∙ 1 ∙ share

read it
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge

We propose an algorithm based on reinforcement learning for solving NP-h...
05/28/2019 ∙ by Kenshin Abe, et al. ∙ 13 ∙ share

read it
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping

Deep neural networks (DNNs) have a high accuracy on image classification...
05/02/2019 ∙ by Xi Yang, et al. ∙ 12 ∙ share

read it
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization

Pairwise similarities and dissimilarities between data points might be e...
04/26/2019 ∙ by Takuya Shimada, et al. ∙ 22 ∙ share

read it
Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics

Imaging flow cytometry shows significant potential for increasing our un...
03/22/2019 ∙ by Hiroaki Adachi, et al. ∙ 0 ∙ share

read it
On Learning from Ghost Imaging without Imaging

Computational ghost imaging is an imaging technique with which an object...
03/14/2019 ∙ by Issei Sato, et al. ∙ 12 ∙ share

read it
On Transformations in Stochastic Gradient MCMC

Stochastic gradient Langevin dynamics (SGLD) is a widely used sampler fo...
03/07/2019 ∙ by Soma Yokoi, et al. ∙ 8 ∙ share

read it
PAC-Bayes Analysis of Sentence Representation

Learning sentence vectors from an unlabeled corpus has attracted attenti...
02/12/2019 ∙ by Kento Nozawa, et al. ∙ 16 ∙ share

read it
Online Multiclass Classification Based on Prediction Margin for Partial Feedback

We consider the problem of online multiclass classification with partial...
02/04/2019 ∙ by Takuo Kaneko, et al. ∙ 20 ∙ share

read it
Multi-level Monte Carlo Variational Inference

In many statistics and machine learning frameworks, stochastic optimizat...
02/01/2019 ∙ by Masahiro Fujisawa, et al. ∙ 16 ∙ share

read it
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization

We consider the semi-supervised ordinal regression problem, where unlabe...
01/31/2019 ∙ by Taira Tsuchiya, et al. ∙ 6 ∙ share

read it
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis

The notion of flat minima has played a key role in the generalization pr...
01/15/2019 ∙ by Yusuke Tsuzuku, et al. ∙ 12 ∙ share

read it
Clipped Matrix Completion: a Remedy for Ceiling Effects

We consider the recovery of a low-rank matrix from its clipped observati...
09/13/2018 ∙ by Takeshi Teshima, et al. ∙ 0 ∙ share

read it
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions

Data-agnostic quasi-imperceptible perturbations on inputs can severely d...
09/11/2018 ∙ by Yusuke Tsuzuku, et al. ∙ 0 ∙ share

read it
Unsupervised Domain Adaptation Based on Source-guided Discrepancy

Unsupervised domain adaptation is the problem setting where data generat...
09/11/2018 ∙ by Seiichi Kuroki, et al. ∙ 2 ∙ share

read it
Frank-Wolfe Stein Sampling

In Bayesian inference, the posterior distributions are difficult to obta...
05/21/2018 ∙ by Futoshi Futami, et al. ∙ 0 ∙ share

read it
Variational Inference for Gaussian Process with Panel Count Data

We present the first framework for Gaussian-process-modulated Poisson pr...
03/12/2018 ∙ by Hongyi Ding, et al. ∙ 0 ∙ share

read it
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model

While crowdsourcing has become an important means to label data, crowdwo...
02/13/2018 ∙ by Hideaki Imamura, et al. ∙ 0 ∙ share

read it
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

High sensitivity of neural networks against malicious perturbations on i...
02/12/2018 ∙ by Yusuke Tsuzuku, et al. ∙ 0 ∙ share

read it
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer

Learning using privileged information is an attractive problem setting t...
02/12/2018 ∙ by Ryosuke Kamesawa, et al. ∙ 0 ∙ share

read it
Variational Inference based on Robust Divergences

Robustness to outliers is a central issue in real-world machine learning...
10/18/2017 ∙ by Futoshi Futami, et al. ∙ 1 ∙ share

read it
On the Model Shrinkage Effect of Gamma Process Edge Partition Models

The edge partition model (EPM) is a fundamental Bayesian nonparametric m...
09/26/2017 ∙ by Iku Ohama, et al. ∙ 0 ∙ share

read it
Expectation Propagation for t-Exponential Family Using Q-Algebra

Exponential family distributions are highly useful in machine learning s...
05/25/2017 ∙ by Futoshi Futami, et al. ∙ 0 ∙ share

read it
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling

Analyzing the underlying structure of multiple time-sequences provides i...
05/19/2017 ∙ by Hongyi Ding, et al. ∙ 0 ∙ share

read it
Stochastic Divergence Minimization for Biterm Topic Model

As the emergence and the thriving development of social networks, a huge...
05/01/2017 ∙ by Zhenghang Cui, et al. ∙ 0 ∙ share

read it
Revisiting Distributionally Robust Supervised Learning in Classification

Distributionally Robust Supervised Learning (DRSL) is necessary for buil...
11/07/2016 ∙ by Weihua Hu, et al. ∙ 0 ∙ share

read it
Reparameterization trick for discrete variables

Low-variance gradient estimation is crucial for learning directed graphi...
11/04/2016 ∙ by Seiya Tokui, et al. ∙ 0 ∙ share

read it
Generative Adversarial Nets from a Density Ratio Estimation Perspective

Generative adversarial networks (GANs) are successful deep generative mo...
10/10/2016 ∙ by Masatoshi Uehara, et al. ∙ 0 ∙ share

read it
Quantum Annealing for Variational Bayes Inference

This paper presents studies on a deterministic annealing algorithm based...
08/09/2014 ∙ by Issei Sato, et al. ∙ 0 ∙ share

read it
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering

We developed a new quantum annealing (QA) algorithm for Dirichlet proces...
05/19/2013 ∙ by Issei Sato, et al. ∙ 0 ∙ share

read it
Rethinking Collapsed Variational Bayes Inference for LDA

We propose a novel interpretation of the collapsed variational Bayes inf...
06/27/2012 ∙ by Issei Sato, et al. ∙ 0 ∙ share

read it
Restricted Collapsed Draw: Accurate Sampling for Hierarchical Chinese Restaurant Process Hidden Markov Models

We propose a restricted collapsed draw (RCD) sampler, a general Markov c...
06/02/2011 ∙ by Takaki Makino, et al. ∙ 0 ∙ share

read it