Search Results for author: Han Bao

Found 35 papers, 8 papers with code

Online Policy Learning from Offline Preferences

no code implementations15 Mar 2024 Guoxi Zhang, Han Bao, Hisashi Kashima

To address this problem, the present study introduces a framework that consolidates offline preferences and \emph{virtual preferences} for PbRL, which are comparisons between the agent's behaviors and the offline data.

Continuous Control

Referee-Meta-Learning for Fast Adaptation of Locational Fairness

no code implementations20 Feb 2024 Weiye Chen, Yiqun Xie, Xiaowei Jia, Erhu He, Han Bao, Bang An, Xun Zhou

When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm.

Decision Making Fairness +1

Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss

no code implementations13 Feb 2024 Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki

We extend the exploit-the-surrogate-gap framework to online structured prediction with \emph{Fenchel--Young losses}, a large family of surrogate losses including the logistic loss for multiclass classification, obtaining finite surrogate regret bounds in various structured prediction problems.

Classification Structured Prediction

Self-attention Networks Localize When QK-eigenspectrum Concentrates

no code implementations3 Feb 2024 Han Bao, Ryuichiro Hataya, Ryo Karakida

To this end, we characterize the notion of attention localization by the eigenspectrum of query-key parameter matrices and reveal that a small eigenspectrum variance leads attention to be localized.

Embarrassingly Simple Text Watermarks

1 code implementation13 Oct 2023 Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada

LLMs can generate texts that cannot be distinguished from human-written texts.

Necessary and Sufficient Watermark for Large Language Models

no code implementations2 Oct 2023 Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada

Although existing watermarking methods have successfully detected texts generated by LLMs, they significantly degrade the quality of the generated texts.

Machine Translation

Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics

no code implementations28 Sep 2023 Han Bao

While learned representations may collapse into a single point due to the lack of the repulsive force at first sight, Tian et al. (2021) revealed through the learning dynamics analysis that the representations can avoid collapse if data augmentation is sufficiently stronger than regularization.

Computational Efficiency Contrastive Learning +2

Estimating Treatment Effects Under Heterogeneous Interference

1 code implementation25 Sep 2023 Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima

One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).

Decision Making

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems

no code implementations8 Aug 2023 Edward Chen, Han Bao, Nam Dinh

The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability.

Out-of-Distribution Detection

BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo

no code implementations9 Apr 2023 Yinhao Li, Jinrong Yang, Jianjian Sun, Han Bao, Zheng Ge, Li Xiao

Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.

3D Object Detection Depth Estimation +2

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

no code implementations20 Jan 2023 Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, Xiaowei Jia

While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts.

Generative Adversarial Network

Robust computation of optimal transport by $β$-potential regularization

no code implementations26 Dec 2022 Shintaro Nakamura, Han Bao, Masashi Sugiyama

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions.

Will Large-scale Generative Models Corrupt Future Datasets?

1 code implementation ICCV 2023 Ryuichiro Hataya, Han Bao, Hiromi Arai

These trends lead us to a research question: "\textbf{will such generated images impact the quality of future datasets and the performance of computer vision models positively or negatively?}"

Image Classification Image Generation

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data

no code implementations30 Sep 2022 Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada

In this study, we propose Momentum Tracking, which is a method with momentum whose convergence rate is proven to be independent of data heterogeneity.

Image Classification

BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo

3 code implementations21 Sep 2022 Yinhao Li, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, Zeming Li

To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.

3D Object Detection Depth Estimation +1

Approximating 1-Wasserstein Distance with Trees

no code implementations24 Jun 2022 Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi

In this paper, we aim to approximate the 1-Wasserstein distance by the tree-Wasserstein distance (TWD), where TWD is a 1-Wasserstein distance with tree-based embedding and can be computed in linear time with respect to the number of nodes on a tree.

On the Surrogate Gap between Contrastive and Supervised Losses

1 code implementation6 Oct 2021 Han Bao, Yoshihiro Nagano, Kento Nozawa

Recent theoretical studies have attempted to explain the benefit of the large negative sample size by upper-bounding the downstream classification loss with the contrastive loss.

Classification Data Augmentation +1

Retrodiction beyond the Heisenberg uncertainty relation

no code implementations26 Sep 2020 Han Bao, Shenchao Jin, Junlei Duan, Suotang Jia, Klaus Mølmer, Heng Shen, Yanhong Xiao

In quantum mechanics, the Heisenberg uncertainty relation presents an ultimate limit to the precision by which one can predict the outcome of position and momentum measurements on a particle.

Quantum Physics

Pairwise Supervision Can Provably Elicit a Decision Boundary

no code implementations11 Jun 2020 Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.

Binary Classification Classification +5

Calibrated Surrogate Losses for Adversarially Robust Classification

no code implementations28 May 2020 Han Bao, Clayton Scott, Masashi Sugiyama

Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns.

Classification General Classification +1

Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

no code implementations7 May 2020 Han Bao, Jinyong Feng, Nam Dinh, Hongbin Zhang

Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions.

Learning from Noisy Similar and Dissimilar Data

no code implementations3 Feb 2020 Soham Dan, Han Bao, Masashi Sugiyama

We perform a detailed investigation of this problem under two realistic noise models and propose two algorithms to learn from noisy S-D data.

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation

no code implementations6 Jan 2020 Han Bao, Nam Dinh, Linyu Lin, Robert Youngblood, Jeffrey Lane, Hongbin Zhang

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities.

Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae

no code implementations7 Nov 2019 Han Bao

Segmentation of objects with various sizes is relatively less explored in medical imaging, and has been very challenging in computer vision tasks in general.


Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning

no code implementations17 Oct 2019 Han Bao, Jinyong Feng, Nam Dinh, Hongbin Zhang

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach.

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification

no code implementations29 May 2019 Han Bao, Masashi Sugiyama

A clue to tackle their direct optimization is a calibrated surrogate utility, which is a tractable lower bound of the true utility function representing a given metric.

Binary Classification Classification +5

Unsupervised Domain Adaptation Based on Source-guided Discrepancy

no code implementations11 Sep 2018 Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama

A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.

Unsupervised Domain Adaptation

Classification from Pairwise Similarity and Unlabeled Data

2 code implementations ICML 2018 Han Bao, Gang Niu, Masashi Sugiyama

Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high.

Classification General Classification +1

Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags

1 code implementation22 Apr 2017 Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.

Content-Based Image Retrieval Multiple Instance Learning +2

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