Learning Graphical Models
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
We adopt a multi-armed bandit framework for this process, enhancing prompt selection based on the returns from online trajectories.
Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
Most reinforcement learning (RL) algorithms hinge on the Markovian assumption, i.e. that the underlying system transitions and rewards are Markovian in some natural notion of (observable)
Discrete Object Generation with Reversible Inductive Construction
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Neural Information Processing Systems http://nips.cc/
Random deep neural networks are biased towards simple functions
Giacomo De Palma, Bobak Kiani, Seth Lloyd
Neural Information Processing Systems http://nips.cc/
Idiographic Personality Gaussian Process for Psychological Assessment
However, existing frameworks face several interrelated limitations.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang, Jun WANG, Zhanxing Zhu
Neural Information Processing Systems http://nips.cc/
