41 citations · 61 across the 9 of their papers we have counts for
9 papers
A Pinch of SFT, A Dash of RL: When Reinforcement Learning Helps Long-Horizon Advertising Agents
Aakash Kolekar, Sahika Genc, Bunyamin Sisman +6
Enterprise analytics agents solve long-horizon tool-use problems over distributed business data, requiring retrieval, reasoning, API calls, code execution, and adaptation to interm…
CRAFT: Learn the Schema, Execute the Plan
Aakash Kolekar, Sahika Genc, Shahriar Shariat +10
Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions. Yet the prevailing deployment p…
Diffusion Models For Multi-Modal Generative Modeling
Changyou Chen, Han Ding, Bunyamin Sisman +5
Diffusion-based generative modeling has been achieving state-of-the-art results on various generation tasks. Most diffusion models, however, are limited to a single-generation mode…
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
Di Jin, Bunyamin Sisman, Hao Wei +2
Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact…
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction
Zhengbao Jiang, Jialong Han, Bunyamin Sisman +1
Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text rela…
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
Zhengyang Wang, Bunyamin Sisman, Hao Wei +2
Entity linkage (EL) is a critical problem in data cleaning and integration. In the past several decades, EL has typically been done by rule-based systems or traditional machine lea…
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Wei Zhang, Hao Wei, Bunyamin Sisman +3
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets re…
Efficient Knowledge Graph Accuracy Evaluation
Junyang Gao, Xian Li, Yifan Ethan Xu +3
Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph. How to obtain statistically meaningful estimates for accu…
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Rakshit Trivedi, Bunyamin Sisman, Jun Ma +3
Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowl…