Search Results for author:
Found 14 papers, 8 papers with code
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.
Neural Structure Learning with Stochastic Differential Equations
Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals.
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.
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.
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions).
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Simultaneous Missing Value Imputation and Structure Learning with Groups
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
Interpreting diffusion score matching using normalizing flow
Specifically, we theoretically prove that DSM (or DSD) is equivalent to the original score matching (or Stein discrepancy) evaluated in the transformed space defined by the normalizing flow, where the diffusion matrix is the inverse of the flow's Jacobian matrix.
Active Slices for Sliced Stein Discrepancy
First, we provide theoretical results stating that the requirement of using optimal slicing directions in the kernelized version of SSD can be relaxed, validating the resulting discrepancy with finite random slicing directions.
Sliced Kernelized Stein Discrepancy
Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality.
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.
Meta-Learning for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has become increasingly popular for simulating posterior samples in large-scale Bayesian modeling.
