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Found 11 papers, 2 papers with code
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
In this framework, we propose sDM, a generic Bayesian approach designed for OPE and OPL, grounded in both algorithmic and theoretical foundations.
Exponential Smoothing for Off-Policy Learning
In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded.
Statistical guarantees for generative models without domination
In this paper, we introduce a convenient framework for studying (adversarial) generative models from a statistical perspective.
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.
Propose, Test, Release: Differentially private estimation with high probability
We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (2009).
Differentially private sub-Gaussian location estimators
We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations.
Learning Nonsymmetric Determinantal Point Processes
Our method imposes a particular decomposition of the nonsymmetric kernel that enables such tractable learning algorithms, which we analyze both theoretically and experimentally.
A nonasymptotic law of iterated logarithm for general M-estimators
M-estimators are ubiquitous in machine learning and statistical learning theory.
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Symmetric determinantal point processes (DPP) are a class of probabilistic models that encode the random selection of items that have a repulsive behavior.
Best Arm Identification for Contaminated Bandits
Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability $\varepsilon$ of generating a sample from an arbitrary contamination distribution instead of the true underlying distribution.
Learning Determinantal Point Processes with Moments and Cycles
Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set of objects is important.
