11 citations · 40 across the 18 of their papers we have counts for
19 papers
MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment
Dongyue Li, Ziniu Zhang, Lu Wang +1
We study learning a mixture of Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous underlying preferences. This problem has m…
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
Dongyue Li, Zechun Liu, Kai Yi +6
Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is…
Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning
Dongyue Li, Zhenshuo Zhang, Minxuan Duan +2
Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning abilities, designed for graph neural…
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
Ziniu Zhang, Zhenshuo Zhang, Dongyue Li +3
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of examples, how can we quickly select out of to…
Efficient Ensemble for Fine-tuning Language Models on Multiple Datasets
Dongyue Li, Ziniu Zhang, Lu Wang +1
This paper develops an ensemble method for fine-tuning a language model to multiple datasets. Existing methods, such as quantized LoRA (QLoRA), are efficient when adapting to a sin…
Split Matching for Inductive Zero-shot Semantic Segmentation
Jialei Chen, Xu Zheng, Dongyue Li +6
Zero-shot Semantic Segmentation (ZSS) aims to segment categories that are not annotated during training. While fine-tuning vision-language models has achieved promising results, th…
Not All Pixels Are Equal: Confidence-Guided Attention for Feature Matching
Dongyue Li
Semi-dense feature matching methods have been significantly advanced by leveraging attention mechanisms to extract discriminative descriptors. However, most existing approaches tre…
CQVPR: Landmark-aware Contextual Queries for Visual Place Recognition
Dongyue Li, Daisuke Deguchi, Hiroshi Murase
Visual Place Recognition (VPR) aims to estimate the location of the given query image within a database of geo-tagged images. To identify the exact location in an image, detecting…
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach
Dongyue Li, Ziniu Zhang, Lu Wang +1
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from auxiliary tasks. This problem has broad applications in NLP,…
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
Dongyue Li, Aneesh Sharma, Hongyang R. Zhang
Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks m…
Learning Tree-Structured Composition of Data Augmentation
Dongyue Li, Kailai Chen, Predrag Radivojac +1
Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transform…
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
Abhinav Nippani, Dongyue Li, Haotian Ju +2
We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning meth…
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Dongyue Li, Haotian Ju, Aneesh Sharma +1
Predicting node labels on a given graph is a widely studied problem with many applications, including community detection and molecular graph prediction. This paper considers predi…
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach
Hongyang R. Zhang, Dongyue Li, Haotian Ju
The training of over-parameterized neural networks has received much study in recent literature. An important consideration is the regularization of over-parameterized networks due…
Optimal Intervention on Weighted Networks via Edge Centrality
Dongyue Li, Tina Eliassi-Rad, Hongyang R. Zhang
Suppose there is a spreading process such as an infectious disease propagating on a graph. How would we reduce the number of affected nodes in the spreading process? This question…
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
Dongyue Li, Huy L. Nguyen, Hongyang R. Zhang
Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks. Yet, naively combining all the source tasks w…
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Haotian Ju, Dongyue Li, Aneesh Sharma +1
Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural…
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
Haotian Ju, Dongyue Li, Hongyang R. Zhang
We consider fine-tuning a pretrained deep neural network on a target task. We study the generalization properties of fine-tuning to understand the problem of overfitting, which has…
Improved Regularization and Robustness for Fine-tuning in Neural Networks
Dongyue Li, Hongyang R. Zhang
A widely used algorithm for transfer learning is fine-tuning, where a pre-trained model is fine-tuned on a target task with a small amount of labeled data. When the capacity of the…