Search Results for author:
Found 25 papers, 6 papers with code
Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension
Despite the recent advancement of Large Langauge Models (LLMs), they struggle with complex queries often involving multiple conditions, common in real-world scenarios.
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
Generative models must ensure both privacy and fairness for Trustworthy AI.
Fair Class-Incremental Learning using Sample Weighting
We then propose a fair class-incremental learning framework that adjusts the training weights of current task samples to change the direction of the average gradient vector and thus reduce the forgetting of underperforming groups and achieve fairness.
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
We study the problem of robust data augmentation for regression tasks in the presence of noisy data.
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
Unlike knowledge graphs, which are also used to evaluate LLMs, relational databases have integrity constraints that can be used to better construct complex in-depth questions and verify answers: (1) functional dependencies can be used to pinpoint critical keywords that an LLM must know to properly answer a given question containing certain attribute values; and (2) foreign key constraints can be used to join relations and construct multi-hop questions, which can be arbitrarily long and used to debug intermediate answers.
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks.
Falcon: Fair Active Learning using Multi-armed Bandits
Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e. g., (attribute=female, label=positive)) that are the most informative for improving fairness.
Quilt: Robust Data Segment Selection against Concept Drifts
However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy.
Personalized DP-SGD using Sampling Mechanisms
Personalized privacy becomes critical in deep learning for Trustworthy AI.
Improving Fair Training under Correlation Shifts
First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness.
XClusters: Explainability-first Clustering
We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering.
iFlipper: Label Flipping for Individual Fairness
We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips.
Redactor: A Data-centric and Individualized Defense Against Inference Attacks
Our experiments show that a probabilistic decision boundary can be a good proxy for labelers, and that our approach is effective in defending against inference attacks and can scale to large data.
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications.
Sample Selection for Fair and Robust Training
In this work, we propose a sample selection-based algorithm for fair and robust training.
RegMix: Data Mixing Augmentation for Regression
Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance.
Responsible AI Challenges in End-to-end Machine Learning
Second, responsible AI must be broadly supported, preferably in all steps of machine learning.
FairBatch: Batch Selection for Model Fairness
We address this problem via the lens of bilevel optimization.
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification.
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models
Instead, we contend that one needs to selectively acquire data and propose Slice Tuner, which acquires possibly-different amounts of data per slice such that the model accuracy and fairness on all slices are optimized.
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning.
FR-GAN: Fair and Robust Training
We consider the problem of fair and robust model training in the presence of data poisoning.
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks.
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data.
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models.
