Issei Sato
53 papers · 2007–2025 · 12 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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Machine Learning
(45)
Optimization & Theory Β· Machine Learning
(34)
Deep Learning
(19)
Artificial Intelligence
(15)
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(13)
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(11)
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(3)
Keywords
bayesian inference
(7)
variational inference
(6)
representation learning
(4)
generalization bound
(4)
neural network optimization
(3)
stochastic gradient descent
(3)
flat minima
(3)
variance reduction
(2)
theoretical analysis
(2)
outlier robustness
(2)
stochastic optimization
(2)
binary classification
(2)
class imbalance
(2)
generalization error
(2)
latent dirichlet allocation
(2)
probabilistic modeling
(2)
online learning
(2)
bayesian nonparametrics
(2)
few-shot learning
(2)
transformer architecture
(2)
Papers
Benign Overfitting in Token Selection of Attention Mechanism
ICML 2025
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
ICLR 2025
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
ICML 2025
On the Optimal Memorization Capacity of Transformers
ICLR 2025
Theoretical Analysis of Hierarchical Language Recognition and Generation by Transformers without Positional Encoding
ACL 2025
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
ICLR 2024
Exploring Weight Balancing on Long-Tailed Recognition Problem
ICLR 2024
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
NeurIPS 2024
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
NeurIPS 2024
Neural Lagrangian Schr\"{o}dinger Bridge: Diffusion Modeling for Population Dynamics
ICLR 2023
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
NeurIPS 2023
A Closer Look at Prototype Classifier for Few-shot Image Classification
NeurIPS 2022
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
ICML 2022
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
NeurIPS 2022
Disentanglement Analysis with Partial Information Decomposition
ICLR 2022
Evaluation Methods for Representation Learning: A Survey
IJCAI 2022
Pairwise Supervision Can Provably Elicit a Decision Boundary
AISTATS 2022
Predictive variational Bayesian inference as risk-seeking optimization
AISTATS 2022
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
ICLR 2021
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
ICML 2021
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
AISTATS 2021
Loss function based second-order Jensen inequality and its application to particle variational inference
NeurIPS 2021
Multilevel Monte Carlo Variational Inference
JMLR 2021
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
NeurIPS 2021
Ξ³-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
AISTATS 2021
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
ICML 2020
Few-shot Domain Adaptation by Causal Mechanism Transfer
ICML 2020
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
ICML 2020
Bayesian Posterior Approximation via Greedy Particle Optimization
AAAI 2019
Clipped Matrix Completion: A Remedy for Ceiling Effects
AAAI 2019
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
CVPR 2019
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
AAAI 2019
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
ICML 2018
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
AISTATS 2018
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
ICML 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
NeurIPS 2018
Variational Inference based on Robust Divergences
AISTATS 2018
A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors
ACML 2017
Averaged Collapsed Variational Bayes Inference
JMLR 2017
Evaluating the Variance of Likelihood-Ratio Gradient Estimators
ICML 2017
Expectation Propagation for t-Exponential Family Using q-Algebra
NeurIPS 2017
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
NeurIPS 2017
Differential Privacy without Sensitivity
NeurIPS 2016
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
NeurIPS 2014
Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process
ICML 2014
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning
EMNLP 2014
Latent Confusion Analysis by Normalized Gamma Construction
ICML 2014
Multi-armed Bandit Problem with Lock-up Periods
ACML 2013
Understanding seed selection in bootstrapping
EMNLP 2013
Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty
COLING 2012
Reducing Wrong Labels in Distant Supervision for Relation Extraction
ACL 2012
Deterministic Single-Pass Algorithm for LDA
NeurIPS 2010
Bayesian Document Generative Model with Explicit Multiple Topics
EMNLP 2007