1 citations · 1 across the 8 of their papers we have counts for
9 papers
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling
Mehrnoosh Mirtaheri, Ryan A. Rossi, Sungchul Kim +4
Temporal Knowledge Graph (TKG) completion models traditionally assume access to the entire graph during training. This overlooks challenges stemming from the evolving nature of TKG…
NotebookOS: A Replicated Notebook Platform for Interactive Training with On-Demand GPUs
Benjamin Carver, Jingyuan Zhang, Haoliang Wang +2
Interactive notebook programming is universal in modern ML and AI workflows, with interactive deep learning training (IDLT) emerging as a dominant use case. To ensure responsivenes…
Pandora with Inaccurate Priors
Kiarash Banihashem, Xiang Chen, MohammadTaghi Hajiaghayi +4
We investigate the role of inaccurate priors for the classical Pandora's box problem. In the classical Pandora's box problem we are given a set of boxes each with a known cost and…
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…
Correlated Stochastic Knapsack with a Submodular Objective
Sheng Yang, Samir Khuller, Sunav Choudhary +2
We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular functi…
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
Atul Sharma, Pranjal Jain, Ashraf Mahgoub +3
Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We…
Graph Deep Factors for Forecasting
Hongjie Chen, Ryan A. Rossi, Kanak Mahadik +2
Deep probabilistic forecasting techniques have recently been proposed for modeling large collections of time-series. However, these techniques explicitly assume either complete ind…
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
Hongjie Chen, Ryan A. Rossi, Kanak Mahadik +1
Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relat…
Fast Distributed Bandits for Online Recommendation Systems
Kanak Mahadik, Qingyun Wu, Shuai Li +1
Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between user…