89 citations · 279 across the 27 of their papers we have counts for
27 papers
Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
Zhihui Xie, Handong Zhao, Tong Yu +1
Large pretrained multilingual language models (ML-LMs) have shown remarkable capabilities of zero-shot cross-lingual transfer, without direct cross-lingual supervision. While these…
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Junda Wu, Tong Yu, Rui Wang +6
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
Kaige Xie, Tong Yu, Haoliang Wang +6
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To effici…
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim +4
We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer…
Better Generative Replay for Continual Federated Learning
Daiqing Qi, Handong Zhao, Sheng Li
Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data. However, existing…
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
Qiucheng Wu, Yujian Liu, Handong Zhao +6
Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key…
Hierarchical Conversational Preference Elicitation with Bandit Feedback
Jinhang Zuo, Songwen Hu, Tong Yu +3
The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recomme…
Bundle MCR: Towards Conversational Bundle Recommendation
Zhankui He, Handong Zhao, Tong Yu +3
Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large out…
Unified Pretraining Framework for Document Understanding
Jiuxiang Gu, Jason Kuen, Vlad I. Morariu +5
Document intelligence automates the extraction of information from documents and supports many business applications. Recent self-supervised learning methods on large-scale unlabel…
Adversarial Memory Networks for Action Prediction
Zhiqiang Tao, Yue Bai, Handong Zhao +3
Action prediction aims to infer the forthcoming human action with partially-observed videos, which is a challenging task due to the limited information underlying early observation…
Neural Point Process for Learning Spatiotemporal Event Dynamics
Zihao Zhou, Xingyi Yang, Ryan Rossi +2
Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However,…
SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen +5
We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework…
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Kai Li, Curtis Wigington, Chris Tensmeyer +5
Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Ex…
Edge: Enriching Knowledge Graph Embeddings with External Text
Saed Rezayi, Handong Zhao, Sungchul Kim +3
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the…
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
Kai Li, Chang Liu, Handong Zhao +2
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled sample…
Insight-centric Visualization Recommendation
Camille Harris, Ryan A. Rossi, Sana Malik +5
Visualization recommendation systems simplify exploratory data analysis (EDA) and make understanding data more accessible to users of all skill levels by automatically generating v…
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Pan Xu, Zheng Wen, Handong Zhao +1
We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a…
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, Handong Zhao +8
Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to gen…
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Jun Yan, Mrigank Raman, Aaron Chan +6
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. However, KG edge (fact) sparsity and noisy edge extraction/…
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Mrigank Raman, Aaron Chan, Siddhant Agarwal +7
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over…
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Xihui Liu, Zhe Lin, Jianming Zhang +4
We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions. It is a challenging task considering…
Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park, Ryan A. Rossi, Zheng Wen +2
Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous Markov decision process tasks. With its fundamental theory and tractable optimal policy,…
Learnable Subspace Clustering
Jun Li, Hongfu Liu, Zhiqiang Tao +2
This paper studies the large-scale subspace clustering (LSSC) problem with million data points. Many popular subspace clustering methods cannot directly handle the LSSC problem alt…
Cross-Domain Document Object Detection: Benchmark Suite and Method
Kai Li, Curtis Wigington, Chris Tensmeyer +6
Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like…