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Found 37 papers, 19 papers with code
DAG Learning on the Permutahedron
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data.
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.
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).
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.
Local Latent Space Bayesian Optimization over Structured Inputs
By reformulating the encoder to function as both an encoder for the DAE globally and as a deep kernel for the surrogate model within a trust region, we better align the notion of local optimization in the latent space with local optimization in the input space.
MPC-Friendly Commitments for Publicly Verifiable Covert Security
We address the problem of efficiently verifying a commitment in a two-party computation.
Operationalizing Complex Causes: A Pragmatic View of Mediation
We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).
Causal Effect Inference for Structured Treatments
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).
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.
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it.
Learning Binary Decision Trees by Argmin Differentiation
We address the problem of learning binary decision trees that partition data for some downstream task.
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.
A Survey on Contextual Embeddings
Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks.
Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all forms of decision-making.
Cumulo: A Dataset for Learning Cloud Classes
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts.
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.
A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.
Generating Molecules via Chemical Reactions
We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.
Gradient Regularized Budgeted Boosting
As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial.
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.
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.
A Generative Model For Electron Paths
Chemical reactions can be described as the stepwise redistribution of electrons in molecules.
Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.
Learning a Generative Model for Validity in Complex Discrete Structures
This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences -- and thus faithfully model discrete objects.
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.
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.
Grammar Variational Autoencoder
Crucially, state-of-the-art methods often produce outputs that are not valid.
Supervised Word Mover's Distance
Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements.
Private Causal Inference
Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few.
Fast Distributed k-Center Clustering with Outliers on Massive Data
Clustering large data is a fundamental problem with a vast number of applications.
Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership.
Differentially Private Bayesian Optimization
The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems.
Image Data Compression for Covariance and Histogram Descriptors
Specifically, we show that we can reduce data sets to 16% and in some cases as little as 2% of their original size, while approximately matching the test error of kNN classification on the full training set.
Cost-Sensitive Tree of Classifiers
Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e. g. search engines and email spam filters).
