7.1k citations · 9.2k across the 24 of their papers we have counts for
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On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
Miroslav Štrupl, Oleg Szehr, Francesco Faccio +3
This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Tran…
Universal Smoothed Score Functions for Generative Modeling
Saeed Saremi, Rupesh Kumar Srivastava, Francis Bach
We consider the problem of generative modeling based on smoothing an unknown density of interest in using factorial kernels with independent Gaussian channels wi…
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley +2
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for give…
Multimeasurement Generative Models
Saeed Saremi, Rupesh Kumar Srivastava
We formally map the problem of sampling from an unknown distribution with a density in to the problem of learning and sampling a smoother density in $\mathbb{R}^{Md}…
Reward-Weighted Regression Converges to a Global Optimum
Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley +2
Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, le…
Provable Robust Classification via Learned Smoothed Densities
Saeed Saremi, Rupesh Srivastava
Smoothing classifiers and probability density functions with Gaussian kernels appear unrelated, but in this work, they are unified for the problem of robust classification. The key…