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Found 62 papers, 24 papers with code
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
This is because coordinate descent iteratively updates all the parameters in the objective until convergence.
Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
In this work, we propose the box-constrained softmax ($\mathrm{BCSoftmax}$) function, a novel generalization of the $\mathrm{Softmax}$ function that explicitly enforces lower and upper bounds on output probabilities.
Dynamic Feature Selection from Variable Feature Sets Using Features of Features
Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance.
Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models
Large Vision-Language Models (LVLMs) integrate image encoders with Large Language Models (LLMs) to process multi-modal inputs and perform complex visual tasks.
Treatment Effect Estimation for Graph-Structured Targets
While most studies focus on treatment effect estimation on individual targets, in specific contexts, there is a necessity to comprehend the treatment effect on a group of targets, especially those that have relationships represented as a graph structure between them.
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
Our contribution is the novel Federated learning with Weighted Cluster Aggregation (FedWCA) method, designed to mitigate both domain shifts and privacy concerns with only unlabeled data.
Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments
To test the effectiveness of these methods, we conducted experiments on GPT-3. 5 and GPT-4 to evaluate the influence of six biases on the outputs before and after applying these methods.
Enhancing Chess Reinforcement Learning with Graph Representation
Our experiments, performed on smaller networks than the initial AlphaZero paper, show that this new architecture outperforms previous architectures with a similar number of parameters, being able to increase playing strength an order of magnitude faster.
Learning Neural Strategy-Proof Matching Mechanism from Examples
We conducted experiments to learn a strategy-proof matching from matching examples with different numbers of agents.
Learning Fair and Preferable Allocations through Neural Network
NRR is built from a differentiable relaxation of RR and can be trained to learn the agent ordering used for RR.
AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses
Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP).
A Generalized Model for Multidimensional Intransitivity
Intransitivity is a critical issue in pairwise preference modeling.
Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment
Crowdsourcing is an easy, cheap, and fast way to perform large scale quality assessment; however, human judgments are often influenced by cognitive biases, which lowers their credibility.
Evaluating Saliency Explanations in NLP by Crowdsourcing
The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications.
Online Policy Learning from Offline Preferences
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.
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective
Source-free Domain Adaptation (SFDA) is an emerging and challenging research area that addresses the problem of unsupervised domain adaptation (UDA) without source data.
Estimating Treatment Effects Under Heterogeneous Interference
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).
Label Selection Approach to Learning from Crowds
A major advantage of the proposed method is that it can be applied to almost all variants of supervised learning problems by simply adding a selector network and changing the objective function for existing models, without explicitly assuming a model of the noise in crowd annotations.
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
Knowledge tracing consists in predicting the performance of some students on new questions given their performance on previous questions, and can be a prior step to optimizing assessment and learning.
Causal Effect Estimation on Hierarchical Spatial Graph Data
To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome.
Mitigating Voter Attribute Bias for Fair Opinion Aggregation
The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data.
Mitigating Observation Biases in Crowdsourced Label Aggregation
Crowdsourcing has been widely used to efficiently obtain labeled datasets for supervised learning from large numbers of human resources at low cost.
Multiview Representation Learning from Crowdsourced Triplet Comparisons
Crowdsourcing has been used to collect data at scale in numerous fields.
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filtering, especially when side information over users or items is available.
Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning
To overcome this drawback, the present study proposes a latent variable model to infer a set of policies from data, which allows an agent to use as behavior policy the policy that best describes a particular trajectory.
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.
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms.
Transfer Learning with Pre-trained Conditional Generative Models
To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL).
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models.
Batch Reinforcement Learning from Crowds
This paper addresses the lack of reward in a batch reinforcement learning setting by learning a reward function from preferences.
Crowdsourcing Evaluation of Saliency-based XAI Methods
In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.
Inter-domain Multi-relational Link Prediction
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities.
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.
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Hawkes processes offer a central tool for modeling the diffusion processes, in which the influence from the past events is described by the triggering kernel.
Computationally Efficient Wasserstein Loss for Structured Labels
The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation, emotion analysis, and semantic segmentation.
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference
In this paper, we consider the problem of estimating the effects of crowd movement guidance from past data.
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.
GraphITE: Estimating Individual Effects of Graph-structured Treatments
Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations.
Chemical Property Prediction Under Experimental Biases
Predicting the chemical properties of compounds is crucial in discovering novel materials and drugs with specific desired characteristics.
CrowDEA: Multi-view Idea Prioritization with Crowds
We propose an approach, called CrowDEA, which estimates the embeddings of the ideas in the multiple-criteria preference space, the best viewpoint for each idea, and preference criterion for each evaluator, to obtain a set of frontier ideas.
Regret Minimization for Causal Inference on Large Treatment Space
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems.
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.
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation
Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains.
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
To avoid restrictive functional assumptions, we define the {\it probability of individual unfairness} (PIU) and solve an optimization problem where PIU's upper bound, which can be estimated from data, is controlled to be close to zero.
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.
Fast Sparse Group Lasso
Block Coordinate Descent is a standard approach to obtain the parameters of Sparse Group Lasso, and iteratively updates the parameters for each parameter group.
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.
Topological Bayesian Optimization with Persistence Diagrams
In this paper, we propose the topological Bayesian optimization, which can efficiently find an optimal solution from structured data using \emph{topological information}.
Theoretical evidence for adversarial robustness through randomization
This paper investigates the theory of robustness against adversarial attacks.
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.
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform.
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
BayesGrad: Explaining Predictions of Graph Convolutional Networks
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well.
Budgeted stream-based active learning via adaptive submodular maximization
In contrast, there have been few methods for stream-based active learning based on adaptive submodularity.
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms.
Statistical Performance of Convex Tensor Decomposition
We analyze the statistical performance of a recently proposed convex tensor decomposition algorithm.
Multi-Task Learning via Conic Programming
In this paper, we propose a novel MTL algorithm that can overcome these problems.
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
In this paper, we propose a direct importance estimation method that does not require the input density estimates.
