Search Results for author:
Found 28 papers, 18 papers with code
User-Side Realization
The problem is, that even if users are dissatisfied, they often do not have the means to resolve their dissatisfaction.
Making Translators Privacy-aware on the User's Side
As a result, users are hesitant to utilize machine translation engines for data demanding high levels of privacy protection, thereby missing out on their benefits.
Embarrassingly Simple Text Watermarks
LLMs can generate texts that cannot be distinguished from human-written texts.
Necessary and Sufficient Watermark for Large Language Models
Although existing watermarking methods have successfully detected texts generated by LLMs, they significantly degrade the quality of the generated texts.
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
These results show that GNNs can fully exploit the graph structure by themselves, and in effect, GNNs can use both the hidden and explicit node features for downstream tasks.
Active Learning from the Web
In this paper, we advocate that such a task-specific pool is not always available and propose the use of a myriad of unlabelled data on the Web for the pool for which active learning is applied.
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
In this study, we propose Momentum Tracking, which is a method with momentum whose convergence rate is proven to be independent of data heterogeneity.
Towards Principled User-side Recommender Systems
This approach opens the door to user-defined fair systems even if the official recommender system of the service is not fair.
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
The main difficulty in investigating the effects is that we need to know counterfactual results, which are not available in reality.
Approximating 1-Wasserstein Distance with Trees
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.
CLEAR: A Fully User-side Image Search System
In this framework, each user builds their own search system that meets their preference with a user-defined scoring function and user-defined interface.
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
In this study, we propose WordTour, unsupervised one-dimensional word embeddings.
Retrieving Black-box Optimal Images from External Databases
We assume the user can access the database through a search query with tight API limits.
Fixed Support Tree-Sliced Wasserstein Barycenter
By contrast, the Wasserstein distance on a tree, called the tree-Wasserstein distance, can be computed in linear time and allows for the fast comparison of a large number of distributions.
Enumerating Fair Packages for Group Recommendations
This can be problematic because a group can be dissatisfied with the recommended package owing to some unobserved reasons, even if the score is high.
Re-evaluating Word Mover's Distance
The original study on WMD reported that WMD outperforms classical baselines such as bag-of-words (BOW) and TF-IDF by significant margins in various datasets.
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?
The key challenge is that a user does not have access to the log data of other users or the latent representations of items.
Supervised Tree-Wasserstein Distance
Specifically, we rewrite the Wasserstein distance on the tree metric by the parent-child relationships of a tree and formulate it as a continuous optimization problem using a contrastive loss.
Feature-Robust Optimal Transport for High-Dimensional Data
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
Poincare: Recommending Publication Venues via Treatment Effect Estimation
We use a bias correction method to estimate the potential impact of choosing a publication venue effectively and to recommend venues based on the potential impact of papers in each venue.
Fast Unbalanced Optimal Transport on a Tree
This study examines the time complexities of the unbalanced optimal transport problems from an algorithmic perspective for the first time.
Feature Robust Optimal Transport for High-dimensional Data
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
A Survey on The Expressive Power of Graph Neural Networks
Graph neural networks (GNNs) are effective machine learning models for various graph learning problems.
Random Features Strengthen Graph Neural Networks
Through experiments, we show that the addition of random features enables GNNs to solve various problems that normal GNNs, including the graph convolutional networks (GCNs) and graph isomorphism networks (GINs), cannot solve.
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
Building on \cite{memoli-2011}, who proposed to represent each point in each distribution as the 1D distribution of its distances to all other points, we introduce in this paper the Anchor Energy (AE) and Anchor Wasserstein (AW) distances, which are respectively the energy and Wasserstein distances instantiated on such representations.
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
We theoretically demonstrate that the most powerful GNN can learn approximation algorithms for the minimum dominating set problem and the minimum vertex cover problem with some approximation ratios with the aid of the theory of distributed local algorithms.
Learning to Sample Hard Instances for Graph Algorithms
We propose HiSampler, the hard instance sampler, to model the hard instance distribution of graph algorithms.
Constant Time Graph Neural Networks
The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems.
