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Found 14 papers, 5 papers with code
Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering
However, data curation for document QA is uniquely challenging because the context (i. e. answer evidence passage) needs to be retrieved from potentially long, ill-formatted documents.
One for All: Simultaneous Metric and Preference Learning over Multiple Users
This paper investigates simultaneous preference and metric learning from a crowd of respondents.
A Blessing of Dimensionality in Membership Inference through Regularization
Is overparameterization a privacy liability?
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Finally, we discuss the impact of the bias of the MLE on the logistic bandit problem, providing an example where $d^2$ lower order regret (cf., it is $d$ for linear bandits) may not be improved as long as the MLE is used and how bias-corrected estimators may be used to make it closer to $d$.
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to memorize the training data (zero error on the training data).
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable setting.
Nearly Optimal Algorithms for Level Set Estimation
The threshold value $\alpha$ can either be \emph{explicit} and provided a priori, or \emph{implicit} and defined relative to the optimal function value, i. e. $\alpha = (1-\epsilon)f(x_\ast)$ for a given $\epsilon > 0$ where $f(x_\ast)$ is the maximal function value and is unknown.
NFT-K: Non-Fungible Tangent Kernels
To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel.
Nearest Neighbor Search Under Uncertainty
Specifically, consider the setting in which an NNS algorithm has access only to a stochastic distance oracle that provides a noisy, unbiased estimate of the distance between any pair of points, rather than the exact distance.
Finding All $\epsilon$-Good Arms in Stochastic Bandits
The pure-exploration problem in stochastic multi-armed bandits aims to find one or more arms with the largest (or near largest) means.
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits
Specifically, our confidence bound avoids a direct dependence on $1/\kappa$, where $\kappa$ is the minimal variance over all arms' reward distributions.
Finding All ε-Good Arms in Stochastic Bandits
Mathematically, the all-{\epsilon}-good arm identification problem presents significant new challenges and surprises that do not arise in the pure-exploration objectives studied in the past.
Learning Nearest Neighbor Graphs from Noisy Distance Samples
We consider the problem of learning the nearest neighbor graph of a dataset of n items.
Learning Low-Dimensional Metrics
This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy of the learned metric relative to the underlying true generative metric.
