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08/31/2023
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospectiv
Background Weight loss trajectories after bariatric surgery vary widely ...
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06/19/2023
AdaStop: sequential testing for efficient and reliable comparisons of Deep RL Agents
The reproducibility of many experimental results in Deep Reinforcement L...
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04/11/2023
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning
Interpretability of AI models allows for user safety checks to build tru...
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10/16/2022
Entropy Regularized Reinforcement Learning with Cascading Networks
Deep Reinforcement Learning (Deep RL) has had incredible achievements on...
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09/20/2022
Soft Action Priors: Towards Robust Policy Transfer
Despite success in many challenging problems, reinforcement learning (RL...
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10/20/2021
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is ma...
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06/08/2021
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions ...
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05/20/2021
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Sparse rewards are double-edged training signals in reinforcement learni...
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02/08/2021
Adversarially Guided Actor-Critic
Despite definite success in deep reinforcement learning problems, actor-...
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11/09/2020
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
In practice, it is quite common to face combinatorial optimization probl...
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10/09/2020
Is Standard Deviation the New Standard? Revisiting the Critic in Deep Policy Gradients
Policy gradient algorithms have proven to be successful in diverse decis...
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08/07/2020
A Machine of Few Words – Interactive Speaker Recognition with Reinforcement Learning
Speaker recognition is a well known and studied task in the speech proce...
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10/04/2019
"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action
The use of Reinforcement Learning (RL) is still restricted to simulation...
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09/26/2019
High-Dimensional Control Using Generalized Auxiliary Tasks
A long-standing challenge in reinforcement learning is the design of fun...
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04/08/2019
Samples are not all useful: Denoising policy gradient updates using variance
Policy gradient algorithms in reinforcement learning rely on efficiently...
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12/15/2018
A Large-Scale Study of Call Graph-based Impact Prediction using Mutation Testing
In software engineering, impact analysis involves predicting the softwar...
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08/03/2018
Visual Reasoning with Multi-hop Feature Modulation
Recent breakthroughs in computer vision and natural language processing ...
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07/23/2018
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Recommender systems objectives can be broadly characterized as modeling ...
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10/28/2015
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and ...
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05/14/2014
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
In many recommendation applications such as news recommendation, the ite...
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01/12/2013
Multiple functional regression with both discrete and continuous covariates
In this paper we present a nonparametric method for extending functional...
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01/12/2013
Functional Regularized Least Squares Classi cation with Operator-valued Kernels
Although operator-valued kernels have recently received increasing inter...
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05/10/2012
A Generalized Kernel Approach to Structured Output Learning
We study the problem of structured output learning from a regression per...
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03/07/2012
Multiple Operator-valued Kernel Learning
Positive definite operator-valued kernels generalize the well-known noti...
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02/29/2012
Fast Reinforcement Learning with Large Action Sets using Error-Correcting Output Codes for MDP Factorization
The use of Reinforcement Learning in real-world scenarios is strongly li...
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08/29/2011