9 citations · 10 across the 6 of their papers we have counts for
8 papers
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Kuilin Chen, Chi-Guhn Lee
Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pret…
Meta-free few-shot learning via representation learning with weight averaging
Kuilin Chen, Chi-Guhn Lee
Recent studies on few-shot classification using transfer learning pose challenges to the effectiveness and efficiency of episodic meta-learning algorithms. Transfer learning approa…
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb, André Barreto, Scott Sanner +1
Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making. The former can be addressed by transfer le…
Attentive Gaussian processes for probabilistic time-series generation
Kuilin Chen, Chi-Guhn Lee
The transduction of sequence has been mostly done by recurrent networks, which are computationally demanding and often underestimate uncertainty severely. We propose a computationa…
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Resolving the exploration-exploitation trade-off remains a fundamental problem in the design and implementation of reinforcement learning (RL) algorithms. In this paper, we focus o…
Bayesian Experience Reuse for Learning from Multiple Demonstrators
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Learning from demonstrations (LfD) improves the exploration efficiency of a learning agent by incorporating demonstrations from experts. However, demonstration data can often come…