5 papers
Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data
Rui Miao, Babak Shahbaba, Annie Qu
Offline reinforcement learning (RL) aims to find optimal policies in dynamic environments in order to maximize the expected total rewards by leveraging pre-collected data. Learning…
ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Ziyi Liang, Hamed Poursiami, Zhishun Yang +5
Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision…
Heterogeneous Graph Alignment for Joint Reasoning and Interpretability
Zahra Moslemi, Ziyi Liang, Norbert Fortin +1
Multi-graph learning is crucial for extracting meaningful signals from collections of heterogeneous graphs. However, effectively integrating information across graphs with differin…
Neural-Inspired Posterior Approximation (NIPA)
Babak Shahbaba, Zahra Moslemi
Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control, including model-based (goa…
Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Ziyi Liang, Annie Qu, Babak Shahbaba
Developing effective multimodal data fusion strategies has become increasingly essential for improving the predictive power of statistical machine learning methods across a wide ra…