Search Results for author:
Found 50 papers, 16 papers with code
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #10 on
Image Classification
on VTAB-1k
(using extra training data)
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine learning.
Ranked #10 on
Conditional Image Generation
on ImageNet 128x128
Conditional Image Generation
Vocal Bursts Intensity Prediction
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
Weakly-Supervised Disentanglement Without Compromises
Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets.
Fast and Provably Good Seedings for k-Means
Seeding - the task of finding initial cluster centers - is critical in obtaining high-quality clusterings for k-Means.
Scalable k-Means Clustering via Lightweight Coresets
As such, they have been successfully used to scale up clustering models to massive data sets.
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
We introduce RecurrentGemma, an open language model which uses Google's novel Griffin architecture.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.
Practical Coreset Constructions for Machine Learning
We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set.
A general class of surrogate functions for stable and efficient reinforcement learning
Common policy gradient methods rely on the maximization of a sequence of surrogate functions.
Decoding a Neural Retriever's Latent Space for Query Suggestion
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.
One-Shot Coresets: The Case of k-Clustering
Scaling clustering algorithms to massive data sets is a challenging task.
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means
In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are *unbounded*.
Horizontally Scalable Submodular Maximization
A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization.
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
We propose a single, practical algorithm to construct strong coresets for a large class of hard and soft clustering problems based on Bregman divergences.
Recent Advances in Autoencoder-Based Representation Learning
Finally, we provide an analysis of autoencoder-based representation learning through the lens of rate-distortion theory and identify a clear tradeoff between the amount of prior knowledge available about the downstream tasks, and how useful the representation is for this task.
Distributed and Provably Good Seedings for k-Means in Constant Rounds
The k-Means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation.
Uniform Deviation Bounds for k-Means Clustering
In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are unbounded.
Disentangling Factors of Variation Using Few Labels
Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.
Precision-Recall Curves Using Information Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace.
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient factors of variation in the data independently.
On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks.
Automatic Shortcut Removal for Self-Supervised Representation Learning
In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation.
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision.
Disentangling Factors of Variations Using Few Labels
Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but still most widely used clustering methods.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Mean-field Games (MFGs) are a continuous approximation of many-agent RL.
Offline Reinforcement Learning as Anti-Exploration
This is the converse of exploration in RL, which favors such actions.
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents
By training 240 representations and over 10, 000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents.
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.
The Visual Task Adaptation Benchmark
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence.
Out-of-Distribution Generalization
reinforcement-learning
+2
vec2text with Round-Trip Translations
We propose a simple data augmentation technique based on round-trip translations and show in extensive experiments that the resulting vec2text model surprisingly leads to vector spaces that fulfill our four desired properties and that this model strongly outperforms both standard and denoising auto-encoders.
On the importance of data collection for training general goal-reaching policies
Are the quantity and quality of data truly transformative to the performance of a general controller?
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input.
Abstractive Text Summarization
Natural Language Inference
+2
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
WARM: On the Benefits of Weight Averaged Reward Models
We identify two primary challenges when designing RMs to mitigate reward hacking: distribution shifts during the RL process and inconsistencies in human preferences.
MusicRL: Aligning Music Generation to Human Preferences
MusicRL is a pretrained autoregressive MusicLM (Agostinelli et al., 2023) model of discrete audio tokens finetuned with reinforcement learning to maximise sequence-level rewards.
Gemma: Open Models Based on Gemini Research and Technology
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
