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Found 28 papers, 5 papers with code
Spatially-Varying Bayesian Predictive Synthesis for Flexible and Interpretable Spatial Prediction
Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model.
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression
We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models.
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE).
Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
One of the longstanding open questions is a tight lower bound on the probability of misidentifying the best arm and a strategy whose upper bound matches the lower bound when the optimal target allocation ratio of arm draws is unknown.
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
Optimal Simple Regret in Bayesian Best Arm Identification
We consider Bayesian best arm identification in the multi-armed bandit problem.
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting
We consider learning causal relationships under conditional moment restrictions.
Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling
We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.
Learning Causal Relationships from Conditional Moment Conditions by Importance Weighting
We consider learning causal relationships under conditional moment conditions.
The Role of Contextual Information in Best Arm Identification
We demonstrate that contextual information can be used to improve the efficiency of the identification of the best marginalized mean reward compared with the results of Garivier & Kaufmann (2016).
Scalable Personalised Item Ranking through Parametric Density Estimation
However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e. g. IRGAN) cannot achieve practical efficiency due to the training of an extra model.
Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability
To mitigate this limitation, we propose another assumption that the average logging policy converges to a time-invariant function and show the doubly robust (DR) estimator's asymptotic normality.
Density-Ratio Based Personalised Ranking from Implicit Feedback
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones.
ATRO: Adversarial Training with a Rejection Option
In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO).
Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy
The goal of off-policy evaluation (OPE) is to evaluate a new policy using historical data obtained via a behavior policy.
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Based on the properties of the evaluation policy, we categorize OPE situations.
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization
To achieve this purpose, we train an agent to maximize the expected quadratic utility function, a common objective of risk management in finance and economics.
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning
In this paper, we suggest expected quadratic utility maximization (EQUM) as a new framework for policy gradient style reinforcement learning (RL) algorithms with mean-variance control.
Learning Classifiers under Delayed Feedback with a Time Window Assumption
We consider training a binary classifier under delayed feedback (\emph{DF learning}).
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation.
Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales
The goal of OPE is to evaluate a new policy using historical data obtained from behavior policies generated by the bandit algorithm.
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using a nonparametric estimator of the density ratio between the historical and evaluation data distributions.
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
In adaptive experimental design, the experimenter is allowed to change the probability of assigning a treatment using past observations for estimating the ATE efficiently.
Model Specification Test with Unlabeled Data: Approach from Covariate Shift
By applying the proposed method, we can obtain a model that predicts the label for the unlabeled test data well without losing the interpretability of the model.
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment
Our main idea is to apply a framework of learning with rejection and adversarial examples to assist in the decision making for such suspicious samples.
Learning from Positive and Unlabeled Data with a Selection Bias
However, this assumption is unrealistic in many instances of PU learning because it fails to capture the existence of a selection bias in the labeling process.
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately.
