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09/15/2022
Rho-Tau Bregman Information and the Geometry of Annealing Paths
Markov Chain Monte Carlo methods for sampling from complex distributions...
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03/23/2022
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are...
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11/04/2021
Model-Free Risk-Sensitive Reinforcement Learning
We extend temporal-difference (TD) learning in order to obtain risk-sens...
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07/01/2021
q-Paths: Generalizing the Geometric Annealing Path using Power Means
Many common machine learning methods involve the geometric annealing pat...
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12/31/2020
Likelihood Ratio Exponential Families
The exponential family is well known in machine learning and statistical...
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12/14/2020
Annealed Importance Sampling with q-Paths
Annealed importance sampling (AIS) is the gold standard for estimating p...
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10/29/2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic Variational Objective (T...
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07/01/2020
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
The recently proposed Thermodynamic Variational Objective (TVO) leverage...
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12/02/2019
Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance f...
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04/15/2019
Exact Rate-Distortion in Autoencoders via Echo Noise
Compression is at the heart of effective representation learning. Howeve...
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05/24/2018
Evading the Adversary in Invariant Representation
Representations of data that are invariant to changes in specified nuisa...
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02/16/2018
