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Found 26 papers, 9 papers with code
MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values
Rule models are often preferred in prediction tasks with tabular inputs as they can be easily interpreted using natural language and provide predictive performance on par with more complex models.
Pure Exploration in Bandits with Linear Constraints
Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone.
Unsupervised domain adaptation by learning using privileged information
The latter is often violated in high-dimensional applications such as image classification which, despite this challenge, continues to serve as inspiration and benchmark for algorithm development.
Multi-Label Image Classification
Unsupervised Domain Adaptation
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
We find that all bounds are vacuous and that sample generalization terms account for much of the observed looseness, especially when these terms interact with measures of domain shift.
Off-Policy Evaluation with Out-of-Sample Guarantees
We consider the problem of evaluating the performance of a decision policy using past observational data.
Sharing pattern submodels for prediction with missing values
Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors.
Case-based off-policy policy evaluation using prototype learning
If the behavior policy is estimated using black-box models, it can be hard to diagnose potential problems and to determine for which inputs the policies differ in their suggested actions and resulting values.
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects
Simulators make unique benchmarks for causal effect estimation since they do not rely on unverifiable assumptions or the ability to intervene on real-world systems, but are often too simple to capture important aspects of real applications.
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time.
Thompson Sampling for Bandits with Clustered Arms
We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered.
Learning Approximate and Exact Numeral Systems via Reinforcement Learning
The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017).
Learning to search efficiently for causally near-optimal treatments
Finding an effective medical treatment often requires a search by trial and error.
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making.
Estimation of Bounds on Potential Outcomes For Decision Making
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics.
Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for non-parametric estimation of causal effects.
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs.
Support and Invertibility in Domain-Invariant Representations
In this work, we give generalization bounds for unsupervised domain adaptation that hold for any representation function by acknowledging the cost of non-invertibility.
Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions
We study heterogeneity in the effect of a mindset intervention on student-level performance through an observational dataset from the National Study of Learning Mindsets (NSLM).
Why Is My Classifier Discriminatory?
Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.
Learning Weighted Representations for Generalization Across Designs
We pose both of these problems as prediction under a shift in design.
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering are beset by local minima, which are sometimes drastically suboptimal.
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication.
Estimating individual treatment effect: generalization bounds and algorithms
We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.
Ranked #3 on
Causal Inference
on Jobs
Learning Representations for Counterfactual Inference
Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
We introduce a unifying generalization of the Lovász theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges.
Generalized Shortest Path Kernel on Graphs
We consider the problem of classifying graphs using graph kernels.
