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06/23/2023
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models
Knowledge distillation is commonly used for compressing neural networks ...
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05/31/2023
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation sys...
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11/07/2022
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining
Given a particular embodiment, we propose a novel method (C3PO) that lea...
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10/21/2022
Decoding a Neural Retriever's Latent Space for Query Suggestion
Neural retrieval models have superseded classic bag-of-words methods suc...
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09/14/2022
vec2text with Round-Trip Translations
We investigate models that can generate arbitrary natural language text ...
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10/10/2021
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
The goal of continuous control is to synthesize desired behaviors. In re...
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08/12/2021
A functional mirror ascent view of policy gradient methods with function approximation
We use functional mirror ascent to propose a general framework (referred...
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07/12/2021
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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06/24/2021
Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation
We present Brax, an open source library for rigid body simulation with a...
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06/11/2021
Offline Reinforcement Learning as Anti-Exploration
Offline Reinforcement Learning (RL) aims at learning an optimal control ...
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06/07/2021
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...
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06/01/2021
What Matters for Adversarial Imitation Learning?
Adversarial imitation learning has become a popular framework for imitat...
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05/25/2021
Hyperparameter Selection for Imitation Learning
We address the issue of tuning hyperparameters (HPs) for imitation learn...
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05/25/2021
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but sti...
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10/27/2020
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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07/28/2020
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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06/10/2020
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successf...
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02/20/2020
Automatic Shortcut Removal for Self-Supervised Representation Learning
In self-supervised visual representation learning, a feature extractor i...
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02/07/2020
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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07/25/2019
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelera...
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06/07/2019
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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05/31/2019
On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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05/29/2019
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient fact...
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05/26/2019
Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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05/03/2019
Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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03/06/2019
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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12/12/2018
Recent Advances in Autoencoder-Based Representation Learning
Learning useful representations with little or no supervision is a key c...
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11/29/2018
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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05/31/2018
Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest...
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11/27/2017
One-Shot Coresets: The Case of k-Clustering
Scaling clustering algorithms to massive data sets is a challenging task...
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03/19/2017
Practical Coreset Constructions for Machine Learning
We investigate coresets - succinct, small summaries of large data sets -...
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02/27/2017
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means
Uniform deviation bounds limit the difference between a model's expected...
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02/27/2017
Scalable and Distributed Clustering via Lightweight Coresets
Coresets are compact representations of data sets such that models train...
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05/31/2016
Horizontally Scalable Submodular Maximization
A variety of large-scale machine learning problems can be cast as instan...
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05/02/2016
Linear-time Outlier Detection via Sensitivity
Outliers are ubiquitous in modern data sets. Distance-based techniques a...
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08/21/2015