Text Mining using PrefixSpan constrained by Item Interval and Item Attribute. - Issei Sato; Yu Hirate; Hayato Yamana (ICDE Workshops, 2006)

Bayesian Document Generative Model with Explicit Multiple Topics. - Issei Sato; Hiroshi Nakagawa (EMNLP-CoNLL, 2007)

Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior. - Issei Sato; Hiroshi Nakagawa (KDD, 2007)

Semi-structure Mining Method for Text Mining with a Chunk-Based Dependency Structure. - Issei Sato; Hiroshi Nakagawa (PAKDD, 2007)

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model. - Issei Sato; Minoru Yoshida; Hiroshi Nakagawa (KDD, 2008)

Person Name Disambiguation in Web Pages Using Social Network, Compound Words and Latent Topics. - Shingo Ono; Issei Sato; Minoru Yoshida; Hiroshi Nakagawa (PAKDD, 2008)

Quantum Annealing for Variational Bayes Inference. - Issei Sato; Kenichi Kurihara; Shu Tanaka; Hiroshi Nakagawa; Seiji Miyashita (UAI, 2009)

ITC-UT: Tweet Categorization by Query Categorization for On-line Reputation Management. - Minoru Yoshida; Shin Matsushima; Shingo Ono; Issei Sato; Hiroshi Nakagawa (CLEF (Notebook Papers/LABs/Workshops), 2010)

Topic models with power-law using Pitman-Yor process. - Issei Sato; Hiroshi Nakagawa (KDD, 2010)

Collusion-resistant privacy-preserving data mining. - Bin Yang; Hiroshi Nakagawa; Issei Sato; Jun Sakuma (KDD, 2010)

Deterministic Single-Pass Algorithm for LDA. - Issei Sato; Kenichi Kurihara; Hiroshi Nakagawa (NIPS, 2010)

Mining Numbers in Text Using Suffix Arrays and Clustering Based on Dirichlet Process Mixture Models. - Minoru Yoshida; Issei Sato; Hiroshi Nakagawa; Akira Terada (PAKDD (2), 2010)

Person name disambiguation by bootstrapping. - Minoru Yoshida; Masaki Ikeda; Shingo Ono; Issei Sato; Hiroshi Nakagawa (SIGIR, 2010)

Succinct Semi-structured Data Mining Based on FREQT. - Issei Sato; Hiroshi Nakagawa (Inf. Media Technol., 2010)

Secure Clustering in Private Networks. - Bin Yang; Issei Sato; Hiroshi Nakagawa (ICDM, 2011)

Probabilistic Matrix Factorization Leveraging Contexts for Unsupervised Relation Extraction. - Shingo Takamatsu; Issei Sato; Hiroshi Nakagawa (PAKDD (1), 2011)

Reducing Wrong Labels in Distant Supervision for Relation Extraction. - Shingo Takamatsu; Issei Sato; Hiroshi Nakagawa (ACL (1), 2012)

Learning from Crowds and Experts. - Hiroshi Kajino; Yuta Tsuboi; Issei Sato; Hisashi Kashima (HCOMP@AAAI, 2012)

Rethinking Collapsed Variational Bayes Inference for LDA. - Issei Sato; Hiroshi Nakagawa (ICML, 2012)

Practical collapsed variational bayes inference for hierarchical dirichlet process. - Issei Sato; Kenichi Kurihara; Hiroshi Nakagawa (KDD, 2012)

Privacy-Preserving EM Algorithm for Clustering on Social Network. - Bin Yang; Issei Sato; Hiroshi Nakagawa (PAKDD (1), 2012)

Multi-armed Bandit Problem with Lock-up Periods. - Junpei Komiyama; Issei Sato; Hiroshi Nakagawa (ACML, 2013)

Understanding seed selection in bootstrapping. - Yo Ehara; Issei Sato; Hidekazu Oiwa; Hiroshi Nakagawa (TextGraphs@EMNLP, 2013)

Quantum annealing for Dirichlet process mixture models with applications to network clustering. - Issei Sato; Shu Tanaka; Kenichi Kurihara; Seiji Miyashita; Hiroshi Nakagawa (Neurocomputing, 2013)

Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning. - Yo Ehara; Yusuke Miyao; Hidekazu Oiwa; Issei Sato; Hiroshi Nakagawa (EMNLP, 2014)

Latent Confusion Analysis by Normalized Gamma Construction. - Issei Sato; Hisashi Kashima; Hiroshi Nakagawa (ICML, 2014)

Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process. - Issei Sato; Hiroshi Nakagawa (ICML, 2014)

Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. - Shinichi Nakajima; Issei Sato; Masashi Sugiyama; Kazuho Watanabe; Hiroko Kobayashi (NIPS, 2014)

Collapsed Variational Bayes Inference of Infinite Relational Model. - Katsuhiko Ishiguro; Issei Sato; Naonori Ueda (CoRR, 2014)

The Hybrid Nested/Hierarchical Dirichlet Process and its Application to Topic Modeling with Word Differentiation. - Tengfei Ma 001; Issei Sato; Hiroshi Nakagawa (AAAI, 2015)

Stochastic Divergence Minimization for Online Collapsed Variational Bayes Zero Inference of Latent Dirichlet Allocation. - Issei Sato; Hiroshi Nakagawa (KDD, 2015)

Locally Optimized Hashing for Nearest Neighbor Search. - Seiya Tokui; Issei Sato; Hiroshi Nakagawa (PAKDD (2), 2015)

Bayesian Differential Privacy on Correlated Data. - Bin Yang; Issei Sato; Hiroshi Nakagawa (SIGMOD Conference, 2015)

Infinite Plaid Models for Infinite Bi-Clustering. - Katsuhiko Ishiguro; Issei Sato; Masahiro Nakano; Akisato Kimura; Naonori Ueda (AAAI, 2016)

Company recommendation for new graduates via implicit feedback multiple matrix factorization with Bayesian optimization. - Masahiro Kazama; Issei Sato; Haruaki Yatabe; Tairiku Ogihara; Tetsuro Onishi; Hiroshi Nakagawa (IEEE BigData, 2016)

Model-Based Approaches for Independence-Enhanced Recommendation. - Toshihiro Kamishima; Shotaro Akaho; Hideki Asoh; Issei Sato (ICDM Workshops, 2016)

Differential Privacy without Sensitivity. - Kentaro Minami; Hiromi Arai; Issei Sato; Hiroshi Nakagawa (NIPS, 2016)

Robust supervised learning under uncertainty in dataset shift. - Weihua Hu; Issei Sato; Masashi Sugiyama (CoRR, 2016)

Reparameterization trick for discrete variables. - Seiya Tokui; Issei Sato (CoRR, 2016)

A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors. - Zeke Xie; Issei Sato (ACML, 2017)

Evaluating the Variance of Likelihood-Ratio Gradient Estimators. - Seiya Tokui; Issei Sato (ICML, 2017)

Lung lesion detection in FDG-PET/CT with Gaussian process regression. - Ryosuke Kamesawa; Issei Sato; Shouhei Hanaoka; Yukihiro Nomura; Mitsutaka Nemoto; Naoto Hayashi; Masashi Sugiyama (Computer-Aided Diagnosis, 2017)

Expectation Propagation for t-Exponential Family Using q-Algebra. - Futoshi Futami; Issei Sato; Masashi Sugiyama (NIPS, 2017)

On the Model Shrinkage Effect of Gamma Process Edge Partition Models. - Iku Ohama; Issei Sato; Takuya Kida; Hiroki Arimura (NIPS, 2017)

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (CoRR, 2017)

Averaged Collapsed Variational Bayes Inference. - Katsuhiko Ishiguro; Issei Sato; Naonori Ueda (J. Mach. Learn. Res., 2017)

Sequential line search for efficient visual design optimization by crowds. - Yuki Koyama 001; Issei Sato; Daisuke Sakamoto; Takeo Igarashi (ACM Trans. Graph., 2017)

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. - Hongyi Ding; Mohammad Emtiyaz Khan; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

Variational Inference based on Robust Divergences. - Futoshi Futami; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

Does Distributionally Robust Supervised Learning Give Robust Classifiers? - Weihua Hu; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2018)

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. - Hideaki Imamura; Issei Sato; Masashi Sugiyama (ICML, 2018)

Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition. - Issei Sato; Yukihiro Nomura; Shouhei Hanaoka; Soichiro Miki; Naoto Hayashi; Osamu Abe; Yoshitaka Masutani (KDD, 2018)

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. - Yusuke Tsuzuku; Issei Sato; Masashi Sugiyama (NeurIPS, 2018)

Bayesian Optimization of HPC Systems for Energy Efficiency. - Takashi Miyazaki; Issei Sato; Nobuyuki Shimizu (ISC, 2018)

Variational Inference for Gaussian Processes with Panel Count Data. - Hongyi Ding; Young Lee; Issei Sato; Masashi Sugiyama (UAI, 2018)

Frank-Wolfe Stein Sampling. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (CoRR, 2018)

Stochastic Divergence Minimization for Biterm Topic Models. - Zhenghang Cui; Issei Sato; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2018)

Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty. - Yo Ehara; Issei Sato; Hidekazu Oiwa; Hiroshi Nakagawa (J. Inf. Process., 2018)

Convex formulation of multiple instance learning from positive and unlabeled bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (Neural Networks, 2018)

Bayesian Posterior Approximation via Greedy Particle Optimization. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. - Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao 002; Junya Honda; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Clipped Matrix Completion: A Remedy for Ceiling Effects. - Takeshi Teshima; Miao Xu 001; Issei Sato; Masashi Sugiyama (AAAI, 2019)

On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions. - Yusuke Tsuzuku; Issei Sato (CVPR, 2019)

Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping. - Xi Yang 017; Bojian Wu; Issei Sato; Takeo Igarashi (CVPR Workshops, 2019)

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Multi-level Monte Carlo Variational Inference. - Masahiro Fujisawa; Issei Sato (CoRR, 2019)

Online Multiclass Classification Based on Prediction Margin for Partial Feedback. - Takuo Kaneko; Issei Sato; Masashi Sugiyama (CoRR, 2019)

PAC-Bayes Analysis of Sentence Representation. - Kento Nozawa; Issei Sato (CoRR, 2019)

On Transformations in Stochastic Gradient MCMC. - Soma Yokoi; Takuma Otsuka; Issei Sato (CoRR, 2019)

On Learning from Ghost Imaging without Imaging. - Issei Sato (CoRR, 2019)

Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics. - Hiroaki Adachi; Yoko Kawamura; Keiji Nakagawa; Ryoichi Horisaki; Issei Sato; Satoko Yamaguchi; Katsuhito Fujiu; Kayo Waki; Hiroyuki Noji; Sadao Ota (CoRR, 2019)

Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. - Kenshin Abe; Zijian Xu 002; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Interactive Subspace Exploration on Generative Image Modelling. - Toby Long Hin Chong; I-Chao Shen; Issei Sato; Takeo Igarashi (CoRR, 2019)

Bayesian interpretation of SGD as Ito process. - Soma Yokoi; Issei Sato (CoRR, 2019)

A Comparison Of Compressed Sensing And Dnn Based Reconstruction For Ghost Motion Imaging. - Mantaro Yamada; Hiroaki Adachi; Ryoichi Horisaki; Issei Sato (ICIP, 2020)

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. - Futoshi Futami; Issei Sato; Masashi Sugiyama (ICML, 2020)

Few-shot Domain Adaptation by Causal Mechanism Transfer. - Takeshi Teshima; Issei Sato; Masashi Sugiyama (ICML, 2020)

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. - Yusuke Tsuzuku; Issei Sato; Masashi Sugiyama (ICML, 2020)

Novel platform for development, training, and validation of computer-assisted detection/diagnosis software. - Yukihiro Nomura; Soichiro Miki; Naoto Hayashi; Shouhei Hanaoka; Issei Sato; Takeharu Yoshikawa; Yoshitaka Masutani; Osamu Abe (Int. J. Comput. Assist. Radiol. Surg., 2020)

Anomaly detection in chest radiographs with a weakly supervised flow-based deep learning method. - Hisaichi Shibata; Shouhei Hanaoka; Yukihiro Nomura; Takahiro Nakao; Issei Sato; Naoto Hayashi; Osamu Abe (CoRR, 2020)

A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. - Zeke Xie; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. - Hideaki Imamura; Nontawat Charoenphakdee; Futoshi Futami; Issei Sato; Junya Honda; Masashi Sugiyama (CoRR, 2020)

Similarity-based Classification: Connecting Similarity Learning to Binary Classification. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (CoRR, 2020)

γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato (CoRR, 2020)

LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. - Kei Mukaiyama; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. - Zeke Xie; Xinrui Wang; Huishuai Zhang; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Classification from Ambiguity Comparisons. - Zhenghang Cui; Issei Sato (CoRR, 2020)

Stable Weight Decay Regularization. - Zeke Xie; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Weak approximation of transformed stochastic gradient MCMC. - Soma Yokoi; Takuma Otsuka; Issei Sato (Mach. Learn., 2020)

Classification from Triplet Comparison Data. - Zhenghang Cui; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2020)

Development of training environment for deep learning with medical images on supercomputer system based on asynchronous parallel Bayesian optimization. - Yukihiro Nomura; Issei Sato; Toshihiro Hanawa; Shouhei Hanaoka; Takahiro Nakao; Tomomi Takenaga; Tetsuya Hoshino; Yuji Sekiya; Soichiro Miki; Takeharu Yoshikawa; Naoto Hayashi; Osamu Abe (J. Supercomput., 2020)

Sequential gallery for interactive visual design optimization. - Yuki Koyama 001; Issei Sato; Masataka Goto (ACM Trans. Graph., 2020)

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato; Masashi Sugiyama (AISTATS, 2021)

Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. - Takahiro Mimori; Keiko Sasada; Hirotaka Matsui; Issei Sato (AISTATS, 2021)

A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. - Zeke Xie; Issei Sato; Masashi Sugiyama (ICLR, 2021)

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. - Nan Lu; Shida Lei; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2021)

Loss function based second-order Jensen inequality and its application to particle variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (NeurIPS, 2021)

Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. - Kento Nozawa; Issei Sato (NeurIPS, 2021)

Toward Neural-Network-Guided Program Synthesis and Verification. - Naoki Kobayashi 001; Taro Sekiyama; Issei Sato; Hiroshi Unno 001 (SAS, 2021)

User interfaces for high-dimensional design problems: from theories to implementations. - Yonghao Yue; Yuki Koyama 001; Issei Sato; Takeo Igarashi (SIGGRAPH Courses, 2021)

Versatile anomaly detection method for medical images with semi-supervised flow-based generative models. - Hisaichi Shibata; Shouhei Hanaoka; Yukihiro Nomura; Takahiro Nakao; Issei Sato; Daisuke Sato; Naoto Hayashi; Osamu Abe (Int. J. Comput. Assist. Radiol. Surg., 2021)

Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content-based Guidance. - Toby Long Hin Chong; I-Chao Shen; Issei Sato; Takeo Igarashi (Comput. Graph. Forum, 2021)

Abelian Neural Networks. - Kenshin Abe; Takanori Maehara; Issei Sato (CoRR, 2021)

Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato (Entropy, 2021)

Multilevel Monte Carlo Variational Inference. - Masahiro Fujisawa; Issei Sato (J. Mach. Learn. Res., 2021)

Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. - Takuya Shimada; Han Bao 002; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Semisupervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. - Zeke Xie; Fengxiang He; Shaopeng Fu; Issei Sato; Dacheng Tao; Masashi Sugiyama (Neural Comput., 2021)

Pairwise Supervision Can Provably Elicit a Decision Boundary. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

Predictive variational Bayesian inference as risk-seeking optimization. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

Disentanglement Analysis with Partial Information Decomposition. - Seiya Tokui; Issei Sato (ICLR, 2022)

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. - Zeke Xie; Xinrui Wang; Huishuai Zhang; Issei Sato; Masashi Sugiyama (ICML, 2022)

Evaluation Methods for Representation Learning: A Survey. - Kento Nozawa; Issei Sato (IJCAI, 2022)

A Closer Look at Prototype Classifier for Few-shot Image Classification. - Mingcheng Hou; Issei Sato (NeurIPS, 2022)

Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective. - Keitaro Sakamoto; Issei Sato (NeurIPS, 2022)

Neural Lagrangian Schrödinger Bridge. - Takeshi Koshizuka; Issei Sato (CoRR, 2022)

Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey. - Kento Nozawa; Issei Sato (CoRR, 2022)

Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection. - Takahiro Suzuki; Shouhei Hanaoka; Issei Sato (CoRR, 2022)

Excess risk analysis for epistemic uncertainty with application to variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (CoRR, 2022)

Active Classification With Uncertainty Comparison Queries. - Zhenghang Cui; Issei Sato (Neural Comput., 2022)

Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics. - Takeshi Koshizuka; Issei Sato (ICLR, 2023)

On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. - Zeke Xie; Zhiqiang Xu; Jingzhao Zhang; Issei Sato; Masashi Sugiyama (NeurIPS, 2023)

Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos. - Takeshi Koshizuka; Masahiro Fujisawa; Yusuke Tanaka; Issei Sato (CoRR, 2023)

Understanding Parameter Saliency via Extreme Value Theory. - Shuo Wang; Issei Sato (CoRR, 2023)

Exploring Weight Balancing on Long-Tailed Recognition Problem. - Naoya Hasegawa; Issei Sato (ICLR, 2024)

Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? - Tokio Kajitsuka; Issei Sato (ICLR, 2024)

Top-Down Bayesian Posterior Sampling for Sum-Product Networks. - Soma Yokoi; Issei Sato (KDD, 2024)

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective. - Takeshi Koshizuka; Masahiro Fujisawa; Yusuke Tanaka; Issei Sato (NeurIPS, 2024)

Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective. - Akiyoshi Tomihari; Issei Sato (NeurIPS, 2024)

Benign or Not-Benign Overfitting in Token Selection of Attention Mechanism. - Keitaro Sakamoto; Issei Sato (CoRR, 2024)

On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding. - Kevin Xu; Issei Sato (CoRR, 2024)

Understanding Knowledge Hijack Mechanism in In-context Learning through Associative Memory. - Shuo Wang; Issei Sato (CoRR, 2024)

End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training. - Keitaro Sakamoto; Issei Sato (Trans. Mach. Learn. Res., 2024)

Theoretical Analysis of Hierarchical Language Recognition and Generation by Transformers without Positional Encoding. - Daichi Hayakawa; Issei Sato (ACL (1), 2025)

Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment. - Naoya Hasegawa; Issei Sato (ICLR, 2025)

On the Optimal Memorization Capacity of Transformers. - Tokio Kajitsuka; Issei Sato (ICLR, 2025)

Understanding Generalization in Physics Informed Models through Affine Variety Dimensions. - Takeshi Koshizuka; Issei Sato (CoRR, 2025)

Understanding Why Adam Outperforms SGD: Gradient Heterogeneity in Transformers. - Akiyoshi Tomihari; Issei Sato (CoRR, 2025)

FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing. - Jinya Sakurai; Issei Sato (CoRR, 2025)

To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers. - Kevin Xu; Issei Sato (CoRR, 2025)

Can Test-time Computation Mitigate Memorization Bias in Neural Symbolic Regression? - Shun Sato; Issei Sato (CoRR, 2025)