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Found 20 papers, 2 papers with code
Improving Adversarial Training using Vulnerability-Aware Perturbation Budget
Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks.
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
There has been considerable recent interest in estimating heterogeneous causal effects.
Vulnerability-Aware Instance Reweighting For Adversarial Training
Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks.
Neuro-Symbolic Execution of Generic Source Code
NI is the first neural model capable of executing Py150 dataset programs, including library functions without concrete inputs, and it can be trained with flexible code understanding objectives.
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations
In this paper, we propose integrating HE into AT with regularization terms that exploit the rich angular information available in the HE framework.
Finding and Listing Front-door Adjustment Sets
Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences.
Causal Effect Identification in Cluster DAGs
Finally, we show that C-DAGs are valid for performing counterfactual inferences about clusters of variables.
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
We study the problem of estimating the density of the causal effect of a binary treatment on a continuous outcome given a binary instrumental variable in the presence of covariates.
Partial Counterfactual Identification from Observational and Experimental Data
This paper investigates the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram.
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Specifically, we propose to leverage causal knowledge by regarding the distributional shifts in subpopulations and deployment environments as the results of interventions on the underlying system.
Partial Identification of Counterfactual Distributions
We show that all counterfactual distributions (over finite observed variables) in an arbitrary causal diagram could be generated by a special family of structural causal models (SCMs), compatible with the same causal diagram, where unobserved (exogenous) variables are discrete, taking values in a finite domain.
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.
Learning Causal Effects via Weighted Empirical Risk Minimization
In this paper, we develop a learning framework that marries two families of methods, benefiting from the generality of the causal identification theory and the effectiveness of the estimators produced based on the principle of ERM.
Supervised Whole DAG Causal Discovery
We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning.
Adjustment Criteria for Recovering Causal Effects from Missing Data
In this paper, we introduce a covariate adjustment formulation for controlling confounding bias in the presence of missing-not-at-random data and develop a necessary and sufficient condition for recovering causal effects using the adjustment.
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Machine learning models are vulnerable to adversarial examples.
Recoverability of Joint Distribution from Missing Data
A probabilistic query may not be estimable from observed data corrupted by missing values if the data are not missing at random (MAR).
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data.
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Using dynamic programming (DP), the fastest known sequential algorithm computes the exact posterior probabilities of structural features in $O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the Bayesian network is $n$ and the in-degree (the number of parents) per node is bounded by a constant $d$.
Graphical Models for Inference with Missing Data
We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random.
