308 citations · 396 across the 11 of their papers we have counts for
11 papers
Automated Few-shot Classification with Instruction-Finetuned Language Models
Rami Aly, Xingjian Shi, Kaixiang Lin +2
A particularly successful class of approaches for few-shot learning combines language models with prompts -- hand-crafted task descriptions that complement data samples. However, d…
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic
Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta +3
The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widesprea…
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
Vasu Sharma, Prasoon Goyal, Kaixiang Lin +3
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving col…
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Xiaofeng Gao, Qiaozi Gao, Ran Gong +3
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural la…
Learning to Act with Affordance-Aware Multimodal Neural SLAM
Zhiwei Jia, Kaixiang Lin, Yizhou Zhao +3
Recent years have witnessed an emerging paradigm shift toward embodied artificial intelligence, in which an agent must learn to solve challenging tasks by interacting with its envi…
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
Tongzhou Mu, Kaixiang Lin, Feiyang Niu +1
We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input…
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
Zhaonan Qu, Kaixiang Lin, Zhaojian Li +2
Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. The characteristics of non-i.i.d. data across t…
Ranking Policy Gradient
Kaixiang Lin, Jiayu Zhou
Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art estimates the optimal action values while it usually involves an extensive search…
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang +2
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical perfo…
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Fengyi Tang, Kaixiang Lin, Ikechukwu Uchendu +2
Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in…
Collaborative Deep Reinforcement Learning
Kaixiang Lin, Shu Wang, Jiayu Zhou
Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from di…