Density Ratio Estimation
24 papers with code • 0 benchmarks • 0 datasets
Estimating the ratio of one density function to the other.
Benchmarks
These leaderboards are used to track progress in Density Ratio Estimation
Most implemented papers
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
When sampling from CcGANs, the superiority of cDR-RS is even more noticeable in terms of both effectiveness and efficiency.
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
TaikiMiyagawa/MSPRT-TANDEM
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We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible.
Mandoline: Model Evaluation under Distribution Shift
If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target.
Featurized Density Ratio Estimation
ermongroup/f-dre
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Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation
Learning from positive and unlabeled (PU) data is an important problem in various applications.
Density Ratio Estimation via Infinitesimal Classification
We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the "time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective.
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation
In this work we present MBORE: multi-objective Bayesian optimisation by density-ratio estimation, and compare it to BO across a range of synthetic and real-world benchmarks.
SIXO: Smoothing Inference with Twisted Objectives
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions.
Batch Bayesian optimisation via density-ratio estimation with guarantees
Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions.
Low Variance Off-policy Evaluation with State-based Importance Sampling
In off-policy reinforcement learning, a behaviour policy performs exploratory interactions with the environment to obtain state-action-reward samples which are then used to learn a target policy that optimises the expected return.
