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Found 41 papers, 14 papers with code
A Unified Combination Framework for Dependent Tests with Applications to Microbiome Association Studies
Compared to the existing $p$-value combination methods, including the vanilla Cauchy combination method, the proposed combination framework can handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power.
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
Machine learning methods often assume that the test data have the same distribution as the training data.
Contrastive Learning on Multimodal Analysis of Electronic Health Records
To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation.
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
Our work \textit{initiates} the theoretical study of multi-party RLHF that explicitly models the diverse preferences of multiple individuals.
Distribution-Free Fair Federated Learning with Small Samples
As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic groups becomes critically important.
Selective Learning: Towards Robust Calibration with Dynamic Regularization
The objective can be understood as seeking a model that fits the ground-truth labels by increasing the confidence while also maximizing the entropy of predicted probabilities by decreasing the confidence.
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
Privacy preservation has become a critical concern in high-dimensional data analysis due to the growing prevalence of data-driven applications.
Can AI Be as Creative as Humans?
With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application.
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo).
Conformal Prediction for Deep Classifier via Label Ranking
In this paper, we empirically and theoretically show that disregarding the probabilities' value will mitigate the undesirable effect of miscalibrated probability values.
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages.
Multi-dimensional domain generalization with low-rank structures
In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data.
What Should Data Science Education Do with Large Language Models?
LLMs can also play a significant role in the classroom as interactive teaching and learning tools, contributing to personalized education.
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks.
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments.
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
The score attack method is based on the tracing attack concept in differential privacy and can be applied to any statistical model with a well-defined score statistic.
HappyMap: A Generalized Multi-calibration Method
Fairness is captured by incorporating demographic subgroups into the class of functions~$\mathcal{C}$.
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
We show that the algorithm can detect the ground-truth pairs and improve performance by fully exploiting unpaired datasets.
FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
Several group fairness notions and algorithms have been proposed.
Reinforcement Learning with Stepwise Fairness Constraints
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise
This opens a promising strategy to first train a feature learner rather than a classifier, and then perform linear probing (last layer retraining) in the test environment.
C-Mixup: Improving Generalization in Regression
In this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve generalization on regression tasks.
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses.
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
The Power of Contrast for Feature Learning: A Theoretical Analysis
Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart.
Understanding Dynamics of Nonlinear Representation Learning and Its Application
Representation learning allows us to automatically discover suitable representations from raw sensory data.
Adversarial Training Helps Transfer Learning via Better Representations
Recent works empirically demonstrate that adversarial training in the source data can improve the ability of models to transfer to new domains.
Meta-Learning with Fewer Tasks through Task Interpolation
Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge.
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees
In this paper, we develop a general framework to design differentially private expectation-maximization (EM) algorithms in high-dimensional latent variable models, based on the noisy iterative hard-thresholding.
A Central Limit Theorem for Differentially Private Query Answering
The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high.
When and How Mixup Improves Calibration
In addition, we study how Mixup improves calibration in semi-supervised learning.
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds
We propose differentially private algorithms for parameter estimation in both low-dimensional and high-dimensional sparse generalized linear models (GLMs) by constructing private versions of projected gradient descent.
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression
Based on the iterative estimators, we further construct debiased estimators and establish their asymptotic normality.
How Does Mixup Help With Robustness and Generalization?
For robustness, we show that minimizing the Mixup loss corresponds to approximately minimizing an upper bound of the adversarial loss.
Interpreting Robust Optimization via Adversarial Influence Functions
Robust optimization has been widely used in nowadays data science, especially in adversarial training.
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables
The key to our approach is a computationally lightweight forward addition algorithm that we developed to recursively extract the functional dependencies between table columns that are scalable to tables with many columns.
Improving Generalization in Meta-learning via Task Augmentation
Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
By refining the "tracing adversary" technique for lower bounds in the theoretical computer science literature, we formulate a general lower bound argument for minimax risks with differential privacy constraints, and apply this argument to high-dimensional mean estimation and linear regression problems.
A Sparse PCA Approach to Clustering
We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method.
