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Found 12 papers, 2 papers with code
Targeted active learning for probabilistic models
A fundamental task in science is to design experiments that yield valuable insights about the system under study.
Simple and near-optimal algorithms for hidden stratification and multi-group learning
Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population.
Bayesian decision-making under misspecified priors with applications to meta-learning
We prove that the expected reward accrued by Thompson sampling (TS) with a misspecified prior differs by at most $\tilde{\mathcal{O}}(H^2 \epsilon)$ from TS with a well specified prior, where $\epsilon$ is the total-variation distance between priors and $H$ is the learning horizon.
Contrastive learning, multi-view redundancy, and linear models
Self-supervised learning is an empirically successful approach to unsupervised learning based on creating artificial supervised learning problems.
Expressivity of expand-and-sparsify representations
The linear functions can be specified explicitly and are easy to learn, and we give bounds on how large $m$ needs to be as a function of the input dimension $d$ and the smoothness of the target function.
Contrastive estimation reveals topic posterior information to linear models
Contrastive learning is an approach to representation learning that utilizes naturally occurring similar and dissimilar pairs of data points to find useful embeddings of data.
Interactive Topic Modeling with Anchor Words
The formalism of anchor words has enabled the development of fast topic modeling algorithms with provable guarantees.
A Bayesian Model of Dose-Response for Cancer Drug Studies
The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases.
Diameter-based Interactive Structure Discovery
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others.
Interactive Structure Learning with Structural Query-by-Committee
In this work, we introduce interactive structure learning, a framework that unifies many different interactive learning tasks.
Structural query-by-committee
In this work, we describe a framework that unifies many different interactive learning tasks.
Diameter-Based Active Learning
To date, the tightest upper and lower-bounds for the active learning of general concept classes have been in terms of a parameter of the learning problem called the splitting index.
