Search Results for author:
Found 91 papers, 42 papers with code
Derivative-free tree optimization for complex systems
Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms.
The Essential Role of Causality in Foundation World Models for Embodied AI
This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these.
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.
Doubly Robust Structure Identification from Temporal Data
Furthermore, potential causes may be correlated in practical applications.
Diffusion Based Causal Representation Learning
Causal reasoning can be considered a cornerstone of intelligent systems.
Causal machine learning for single-cell genomics
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells.
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
DRCFS: Doubly Robust Causal Feature Selection
Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science.
Understanding Causality with Large Language Models: Feasibility and Opportunities
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.
Federated Causal Discovery From Interventions
We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task.
Learning Latent Structural Causal Models
For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
Latent Variable Models for Bayesian Causal Discovery
Learning predictors that do not rely on spurious correlations involves building causal representations.
Invariant Causal Mechanisms through Distribution Matching
Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks.
Diffusion Models for Video Prediction and Infilling
By varying the mask we condition on, the model is able to perform video prediction, infilling, and upsampling.
Ranked #2 on
Video Generation
on BAIR Robot Pushing
On the Generalization and Adaption Performance of Causal Models
Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization.
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data.
Interventions, Where and How? Experimental Design for Causal Models at Scale
Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.
Bayesian Structure Learning with Generative Flow Networks
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks
Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge.
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations.
Physical Derivatives: Computing policy gradients by physical forward-propagation
Model-free and model-based reinforcement learning are two ends of a spectrum.
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
Boxhead: A Dataset for Learning Hierarchical Representations
Disentanglement is hypothesized to be beneficial towards a number of downstream tasks.
On the interventional consistency of autoencoders
Autoencoders have played a crucial role in the field of representation learning since its inception, proving to be a flexible learning scheme able to accommodate various notions of optimality of the representation.
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
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.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure.
Exploring the Latent Space of Autoencoders with Interventional Assays
Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods.
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.
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
Representation Learning in Continuous-Time Score-Based Generative Models
Score-based methods represented as stochastic differential equations on a continuous time domain have recently proven successful as a non-adversarial generative model.
Diffusion-Based Representation Learning
In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising.
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak.
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.
Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and developed separately.
Dependency Structure Discovery from Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.
Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic medical images are similar to those that would have been derived from real imaging data.
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.
On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning.
Function Contrastive Learning of Transferable Meta-Representations
This \emph{meta-representation}, which is computed from a few observed examples of the underlying function, is learned jointly with the predictive model.
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment.
Function Contrastive Learning of Transferable Representations
Few-shot-learning seeks to find models that are capable of fast-adaptation to novel tasks which are not encountered during training.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.
TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
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.
S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.
On Disentangled Representations Learned From Correlated Data
The focus of disentanglement approaches has been on identifying independent factors of variation in data.
Structure by Architecture: Structured Representations without Regularization
We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling.
A machine learning route between band mapping and band structure
Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.
Data Analysis, Statistics and Probability Materials Science Computational Physics
Clinical Predictive Models for COVID-19: Systematic Study
Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care.
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.
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations.
Causal models for dynamical systems
In this chapter, we provide a natural and straight-forward extension of this concept to dynamical systems, focusing on continuous time models.
Methodology Dynamical Systems
Learning Neural Causal Models from Unknown Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.
Learning by shaking: Computing policy gradients by physical forward-propagation
Model-free and model-based reinforcement learning are two ends of a spectrum.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Multidimensional Contrast Limited Adaptive Histogram Equalization
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision.
Disentangled State Space Representations
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.
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.
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.
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.
DISENTANGLED STATE SPACE MODELS: UNSUPERVISED LEARNING OF DYNAMICS ACROSS HETEROGENEOUS ENVIRONMENTS
To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM).
Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Stochastic differential equations are an important modeling class in many disciplines.
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting.
Bayesian Online Prediction of Change Points
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.
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.
Automatic human sleep stage scoring using Deep Neural Networks
The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks.
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
We introduce a method which enables a recurrent dynamics model to be temporally abstract.
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Parameter identification and comparison of dynamical systems is a challenging task in many fields.
Efficient and Flexible Inference for Stochastic Systems
Many real world dynamical systems are described by stochastic differential equations.
Scalable Variational Inference for Dynamical Systems
That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
The essence of gradient matching is to model the prior over state variables as a Gaussian process which implies that the joint distribution given the ODE's and GP kernels is also Gaussian distributed.
Model Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for supervised learning.
Multi-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology.
