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Found 8 papers, 3 papers with code
Covariance-Adaptive Least-Squares Algorithm for Stochastic Combinatorial Semi-Bandits
We address the problem of stochastic combinatorial semi-bandits, where a player can select from P subsets of a set containing d base items.
Maximizing the Success Probability of Policy Allocations in Online Systems
The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users.
Nested bandits
In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply closely correlated losses that may confound standard discrete choice models and bandit algorithms.
Diverse Weight Averaging for Out-of-Distribution Generalization
Standard neural networks struggle to generalize under distribution shifts in computer vision.
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the granular level.
Zeroth-order non-convex learning via hierarchical dual averaging
We propose a hierarchical version of dual averaging for zeroth-order online non-convex optimization - i. e., learning processes where, at each stage, the optimizer is facing an unknown non-convex loss function and only receives the incurred loss as feedback.
Online non-convex optimization with imperfect feedback
We consider the problem of online learning with non-convex losses.
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains.
