5 papers
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Xiangchen Song, Zijian Li, Guangyi Chen +4
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctr…
Temporally Disentangled Representation Learning under Unknown Nonstationarity
Xiangchen Song, Weiran Yao, Yewen Fan +5
In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-re…
AgentKit: Structured LLM Reasoning with Dynamic Graphs
Yue Wu, Yewen Fan, So Yeon Min +6
We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" f…
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
Yewen Fan, Nian Si, Xiangchen Song +1
The adoption of deep learning across various fields has been extensive, yet there is a lack of focus on evaluating the performance of deep learning pipelines. Typically, with the i…