Search Results for author:
Found 21 papers, 8 papers with code
Alpha-Divergences in Variational Dropout
We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
GAN Q-learning
Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
EmojiGAN: learning emojis distributions with a generative model
Generative models have recently experienced a surge in popularity due to the development of more efficient training algorithms and increasing computational power.
Exploring attention mechanism for acoustic-based classification of speech utterances into system-directed and non-system-directed
This would require the VA to have the capability to detect the speech that is being directed at it and respond accordingly.
Leveraging exploration in off-policy algorithms via normalizing flows
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios.
Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization
We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning.
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
We propose a general framework for policy representation for reinforcement learning tasks.
Deep Reinforcement and InfoMax Learning
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving and adapting to new RL problems.
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime.
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Targeting immediately measurable proxies such as clicks can lead to suboptimal recommendations due to misalignment with the long-term metric.
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
A highly desirable property of a reinforcement learning (RL) agent -- and a major difficulty for deep RL approaches -- is the ability to generalize policies learned on a few tasks over a high-dimensional observation space to similar tasks not seen during training.
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
We show that performance of online algorithms for generalization in RL can be hindered in the offline setting due to poor estimation of similarity between observations.
The Sandbox Environment for Generalizable Agent Research (SEGAR)
A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress.
Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata
Although WFAs have been extended to deal with continuous input data, namely continuous WFAs (CWFAs), it is still unclear how to approximate density functions over sequences of continuous random variables using WFA-based models, due to the limitation on the expressiveness of the model as well as the tractability of approximating density functions via CWFAs.
Contrastive Value Learning: Implicit Models for Simple Offline RL
In this paper, we propose Contrastive Value Learning (CVL), which learns an implicit, multi-step model of the environment dynamics.
Accelerating exploration and representation learning with offline pre-training
In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
Value function estimation using conditional diffusion models for control
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data.
Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks.
