Olivier Bachem

research

∙ 05/31/2023

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback

Despite the seeming success of contemporary grounded text generation sys...

0 Paul Roit, et al. ∙

research

∙ 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...

0 Alexis Jacq, et al. ∙

research

∙ 10/21/2022

Decoding a Neural Retriever's Latent Space for Query Suggestion

Neural retrieval models have superseded classic bag-of-words methods suc...

0 Leonard Adolphs, et al. ∙

research

∙ 09/14/2022

vec2text with Round-Trip Translations

We investigate models that can generate arbitrary natural language text ...

0 Geoffrey Cideron, et al. ∙

research

∙ 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...

0 Shixiang Shane Gu, et al. ∙

research

∙ 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...

13 Sharan Vaswani, et al. ∙

research

∙ 07/12/2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

Learning data representations that are useful for various downstream tas...

48 Andrea Dittadi, et al. ∙

research

∙ 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...

0 C. Daniel Freeman, et al. ∙

research

∙ 06/11/2021

Offline Reinforcement Learning as Anti-Exploration

Offline Reinforcement Learning (RL) aims at learning an optimal control ...

0 Shideh Rezaeifar, et al. ∙

research

∙ 06/07/2021

Concave Utility Reinforcement Learning: the Mean-field Game viewpoint

Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...

0 Matthieu Geist, et al. ∙

research

∙ 06/01/2021

What Matters for Adversarial Imitation Learning?

Adversarial imitation learning has become a popular framework for imitat...

0 Manu Orsini, et al. ∙

research

∙ 05/25/2021

Hyperparameter Selection for Imitation Learning

We address the issue of tuning hyperparameters (HPs) for imitation learn...

7 Léonard Hussenot, et al. ∙

research

∙ 05/25/2021

Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets

Hierarchical Agglomerative Clustering (HAC) is one of the oldest but sti...

12 Baris Sumengen, et al. ∙

research

∙ 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...

27 Francesco Locatello, et al. ∙

research

∙ 07/28/2020

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...

15 Francesco Locatello, et al. ∙

research

∙ 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...

0 Marcin Andrychowicz, et al. ∙

research

∙ 02/20/2020

Automatic Shortcut Removal for Self-Supervised Representation Learning

In self-supervised visual representation learning, a feature extractor i...

0 Matthias Minderer, et al. ∙

research

∙ 02/07/2020

Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by obs...

20 Francesco Locatello, et al. ∙

research

∙ 07/25/2019

Google Research Football: A Novel Reinforcement Learning Environment

Recent progress in the field of reinforcement learning has been accelera...

16 Karol Kurach, et al. ∙

research

∙ 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...

4 Muhammad Waleed Gondal, et al. ∙

research

∙ 05/31/2019

On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...

6 Francesco Locatello, et al. ∙

research

∙ 05/29/2019

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

A disentangled representation encodes information about the salient fact...

3 Sjoerd van Steenkiste, et al. ∙

research

∙ 05/26/2019

Evaluating Generative Models Using Divergence Frontiers

Despite the tremendous progress in the estimation of generative models, ...

0 Josip Djolonga, et al. ∙

research

∙ 05/03/2019

Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...

12 Francesco Locatello, et al. ∙

research

∙ 03/06/2019

High-Fidelity Image Generation With Fewer Labels

Deep generative models are becoming a cornerstone of modern machine lear...

14 Mario Lucic, et al. ∙

research

∙ 12/12/2018

Recent Advances in Autoencoder-Based Representation Learning

Learning useful representations with little or no supervision is a key c...

0 Michael Tschannen, et al. ∙

research

∙ 11/29/2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...

4 Francesco Locatello, et al. ∙

research

∙ 05/31/2018

Assessing Generative Models via Precision and Recall

Recent advances in generative modeling have led to an increased interest...

0 Mehdi S. M. Sajjadi, et al. ∙

research

∙ 11/27/2017

One-Shot Coresets: The Case of k-Clustering

Scaling clustering algorithms to massive data sets is a challenging task...

0 Olivier Bachem, et al. ∙

research

∙ 03/19/2017

Practical Coreset Constructions for Machine Learning

We investigate coresets - succinct, small summaries of large data sets -...

0 Olivier Bachem, et al. ∙

research

∙ 02/27/2017

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Uniform deviation bounds limit the difference between a model's expected...

0 Olivier Bachem, et al. ∙

research

∙ 02/27/2017

Scalable and Distributed Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models train...

0 Olivier Bachem, et al. ∙

research

∙ 05/31/2016

Horizontally Scalable Submodular Maximization

A variety of large-scale machine learning problems can be cast as instan...

0 Mario Lucic, et al. ∙

research

∙ 05/02/2016

Linear-time Outlier Detection via Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques a...

0 Mario Lucic, et al. ∙

research

∙ 08/21/2015

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...

0 Mario Lucic, et al. ∙

Olivier Bachem

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