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Found 8 papers, 3 papers with code
Distributional Dataset Distillation with Subtask Decomposition
What does a neural network learn when training from a task-specific dataset?
Riemann-Lebesgue Forest for Regression
We propose a novel ensemble method called Riemann-Lebesgue Forest (RLF) for regression.
Distinguishing the Knowable from the Unknowable with Language Models
We study the feasibility of identifying epistemic uncertainty (reflecting a lack of knowledge), as opposed to aleatoric uncertainty (reflecting entropy in the underlying distribution), in the outputs of large language models (LLMs) over free-form text.
On Subagging Boosted Probit Model Trees
With the insight of variance-bias decomposition, we design a new hybrid bagging-boosting algorithm named SBPMT for classification problems.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or geometric domains.
Benefits of Permutation-Equivariance in Auction Mechanisms
Designing an incentive-compatible auction mechanism that maximizes the auctioneer's revenue while minimizes the bidders' ex-post regret is an important yet intricate problem in economics.
