
Offline Reinforcement Learning as AntiExploration
Offline Reinforcement Learning (RL) aims at learning an optimal control ...
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Concave Utility Reinforcement Learning: the Meanfield Game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...
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What Matters for Adversarial Imitation Learning?
Adversarial imitation learning has become a popular framework for imitat...
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Hyperparameter Selection for Imitation Learning
We address the issue of tuning hyperparameters (HPs) for imitation learn...
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Scaling Hierarchical Agglomerative Clustering to Billionsized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but sti...
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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What Matters In OnPolicy Reinforcement Learning? A LargeScale Empirical Study
In recent years, onpolicy reinforcement learning (RL) has been successf...
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Automatic Shortcut Removal for SelfSupervised Representation Learning
In selfsupervised visual representation learning, a feature extractor i...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelera...
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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
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On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient fact...
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Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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HighFidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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Recent Advances in AutoencoderBased Representation Learning
Learning useful representations with little or no supervision is a key c...
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
In recent years, the interest in unsupervised learning of disentangled r...
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Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest...
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OneShot Coresets: The Case of kClustering
Scaling clustering algorithms to massive data sets is a challenging task...
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Practical Coreset Constructions for Machine Learning
We investigate coresets  succinct, small summaries of large data sets ...
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Uniform Deviation Bounds for Unbounded Loss Functions like kMeans
Uniform deviation bounds limit the difference between a model's expected...
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Scalable and Distributed Clustering via Lightweight Coresets
Coresets are compact representations of data sets such that models train...
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Horizontally Scalable Submodular Maximization
A variety of largescale machine learning problems can be cast as instan...
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Lineartime Outlier Detection via Sensitivity
Outliers are ubiquitous in modern data sets. Distancebased techniques a...
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Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Coresets are efficient representations of data sets such that models tra...
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Olivier Bachem
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