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Found 7 papers, 1 papers with code
TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers.
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
Confirming this, under a gaussian mixture setting, we show that the optimal SVM classifier for balanced accuracy needs to be adaptive to the class attributes.
FedYolo: Augmenting Federated Learning with Pretrained Transformers
These insights on scale and modularity motivate a new federated learning approach we call "You Only Load Once" (FedYolo): The clients load a full PTF model once and all future updates are accomplished through communication-efficient modules with limited catastrophic-forgetting, where each task is assigned to its own module.
AutoBalance: Optimized Loss Functions for Imbalanced Data
Our experimental findings are complemented with theoretical insights on loss function design and the benefits of train-validation split.
Post-hoc Models for Performance Estimation of Machine Learning Inference
Estimating how well a machine learning model performs during inference is critical in a variety of scenarios (for example, to quantify uncertainty, or to choose from a library of available models).
On the Role of Dataset Quality and Heterogeneity in Model Confidence
We demonstrate that this leads to heterogenous confidence/accuracy behavior in the test data and is poorly handled by the standard calibration algorithms.
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression
Evaluations on synthetic and real datasets demonstrate that algorithm is competitive with current state-of-the-art and accurately learns feature nonlinearities.
