83 citations · 532 across the 70 of their papers we have counts for
12 papers · 1 filter
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
Maryam Hashemzadeh, Greta Kaufeld, Martha White +2
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. Howev…
Towards Safe Policy Improvement for Non-Stationary MDPs
Yash Chandak, Scott M. Jordan, Georgios Theocharous +2
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that a…
Towards a practical measure of interference for reinforcement learning
Vincent Liu, Adam White, Hengshuai Yao +1
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. But, before we overcome interference we must understand it b…
Understanding and Mitigating the Limitations of Prioritized Experience Replay
Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand +4
Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical u…
Selective Dyna-style Planning Under Limited Model Capacity
Zaheer Abbas, Samuel Sokota, Erin J. Talvitie +1
In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may s…
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg +3
It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems…
Learning Causal Models Online
Khurram Javed, Martha White, Yoshua Bengio
Predictive models -- learned from observational data not covering the complete data distribution -- can rely on spurious correlations in the data for making predictions. These corr…
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
Farzane Aminmansour, Taher Jafferjee, Ehsan Imani +3
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environme…
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Yash Satsangi, Sungsu Lim, Shimon Whiteson +2
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty. For exa…
Optimizing for the Future in Non-Stationary MDPs
Yash Chandak, Georgios Theocharous, Shiv Shankar +3
Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process i…
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe +1
Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce over…
An implicit function learning approach for parametric modal regression
Yangchen Pan, Ehsan Imani, Martha White +1
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because…