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Found 10 papers, 0 papers with code
Learning from others' mistakes: Finetuning machine translation models with span-level error annotations
Despite growing interest in incorporating feedback to improve language models, most efforts focus only on sequence-level annotations.
Don't Throw Away Data: Better Sequence Knowledge Distillation
A critical component in knowledge distillation is the means of coupling the teacher and student.
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance.
Fourier Representations for Black-Box Optimization over Categorical Variables
In order to improve the performance and sample efficiency of such algorithms, we propose to use existing methods in conjunction with a surrogate model for the black-box evaluations over purely categorical variables.
Combinatorial Black-Box Optimization with Expert Advice
We consider the problem of black-box function optimization over the boolean hypercube.
Alternating Linear Bandits for Online Matrix-Factorization Recommendation
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time.
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices
Different nodes have access to different features, as well as access to potentially different computation and energy resources.
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions.
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts.
Masking Strategies for Image Manifolds
We consider the problem of selecting an optimal mask for an image manifold, i. e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data.
