Search Results for author:
Found 41 papers, 16 papers with code
Ranking relations using analogies in biological and information networks
Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?
Thinning Measurement Models and Questionnaire Design
In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features.
Flexible sampling of discrete data correlations without the marginal distributions
Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction.
Causal Inference through a Witness Protection Program
The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths.
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure.
Bayesian Inference in Cumulative Distribution Fields
Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008).
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions.
Observational-Interventional Priors for Dose-Response Learning
In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes.
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences.
Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense.
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification.
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations.
Two-way sparsity for time-varying networks, with applications in genomics
Separation of these two types of sparsity is achieved with the introduction of a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling.
Methodology
Alpha-Beta Divergence For Variational Inference
This paper introduces a variational approximation framework using direct optimization of what is known as the {\it scale invariant Alpha-Beta divergence} (sAB divergence).
Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.
Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs.
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs).
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN).
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.
Counterfactual Distribution Regression for Structured Inference
The inference problem is how information concerning perturbations, with particular covariates such as location and time, can be generalized to predict the effect of novel perturbations.
Adversarial recovery of agent rewards from latent spaces of the limit order book
In this paper, we explore whether adversarial inverse RL algorithms can be adapted and trained within such latent space simulations from real market data, while maintaining their ability to recover agent rewards robust to variations in the underlying dynamics, and transfer them to new regimes of the original environment.
Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainability.
Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all forms of decision-making.
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.
A Class of Algorithms for General Instrumental Variable Models
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.
Causal Effect Inference for Structured Treatments
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).
Operationalizing Complex Causes: A Pragmatic View of Mediation
We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).
When Do Flat Minima Optimizers Work?
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.
Stochastic Causal Programming for Bounding Treatment Effects
Causal effect estimation is important for many tasks in the natural and social sciences.
The Causal Marginal Polytope for Bounding Treatment Effects
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.
Causal discovery under a confounder blanket
Under a structural assumption called the $\textit{confounder blanket principle}$, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity.
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices
We run the approach using a pose estimation task for a robotic arm and compare the results in a high-end device and a constrained device.
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error.
Causal Machine Learning: A Survey and Open Problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases
The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.
Counterfactual Fairness Is Not Demographic Parity, and Other Observations
Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care.
Bounding Causal Effects with Leaky Instruments
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding.
