173 citations · 644 across the 150 of their papers we have counts for
15 papers · 1 filter
The Missing Coefficients: Bayesian Pairwise Merging for Model Personalization
Yaling Shen, Tongtong Wu, Siyuan Yan +1
How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-specialized experts, given a vec…
Evidence-based Distributional Alignment for Large Language Models
Viet-Thanh Pham, Lizhen Qu, Zhuang Li +1
Distributional alignment enables large language models (LLMs) to predict how a target population distributes its responses across answer options, rather than collapsing disagreemen…
Beyond Imitation: Recovering Dense Rewards from Demonstrations
Jiangnan Li, Thuy-Trang Vu, Ehsan Abbasnejad +1
Conventionally, supervised fine-tuning (SFT) is treated as a simple imitation learning process that only trains a policy to imitate expert behavior on demonstration datasets. In th…
Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Rihui Jin, Zheyu Xin, Xing Xie +6
Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to t…
Are Graph Attention Networks Able to Model Structural Information?
Farshad Noravesh, Reza Haffari, Layki Soon +1
Graph Attention Networks (GATs) have emerged as powerful models for learning expressive representations from such data by adaptively weighting neighboring nodes through attention m…
LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation
Farshad Noravesh, Reza Haffari, Layki Soon +1
Hierarchical graph pooling(HGP) are designed to consider the fact that conventional graph neural networks(GNN) are inherently flat and are also not multiscale. However, most HGP me…
A Minimal Approach for Natural Language Action Space in Text-based Games
Dongwon Kelvin Ryu, Meng Fang, Shirui Pan +2
Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling…
Active Continual Learning: On Balancing Knowledge Retention and Learnability
Thuy-Trang Vu, Shahram Khadivi, Mahsa Ghorbanali +2
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the traini…
An Additive Instance-Wise Approach to Multi-class Model Interpretation
Vy Vo, Van Nguyen, Trung Le +4
Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying expla…
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Miao Zhang, Jilin Hu, Steven Su +4
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and…
Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He, Islam Nassar, Jamie Kiros +2
This paper studies the use of language models as a source of synthetic unlabeled text for NLP. We formulate a general framework called ``generate, annotate, and learn (GAL)'' to ta…
Multi-objective semi-supervised clustering to identify health service patterns for injured patients
Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Gholamreza Haffari +1
This study develops a pattern recognition method that identifies patterns based on their similarity and their association with the outcome of interest. The practical purpose of dev…
Medical Multimodal Classifiers Under Scarce Data Condition
Faik Aydin, Maggie Zhang, Michelle Ananda-Rajah +1
Data is one of the essential ingredients to power deep learning research. Small datasets, especially specific to medical institutes, bring challenges to deep learning training stag…
Structured Prediction of Sequences and Trees using Infinite Contexts
Ehsan Shareghi, Gholamreza Haffari, Trevor Cohn +1
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality a…
Analysis of Semi-Supervised Learning with the Yarowsky Algorithm
Gholam Reza Haffari, Anoop Sarkar
The Yarowsky algorithm is a rule-based semi-supervised learning algorithm that has been successfully applied to some problems in computational linguistics. The algorithm was not ma…