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Found 6 papers, 1 papers with code
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels
Large language models (LLMs) are being increasingly tuned to power complex generation tasks such as writing, fact-seeking, querying and reasoning.
Hardness of Learning Boolean Functions from Label Proportions
This is in contrast with the work of (Saket, NeurIPS'21) which gave a $(2/5)$-approximation for learning ORs using a halfspace.
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
One of the unique properties of tabular LLP is the ability to create feature bags where all the instances in a bag have the same value for a given feature.
Label Differential Privacy via Aggregation
Further, the $\ell_2^2$-regressor which minimizes the loss on the aggregated dataset has a loss within $\left(1 + o(1)\right)$-factor of the optimum on the original dataset w. p.
Multi-Variate Time Series Forecasting on Variable Subsets
Through systematic experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover close to 95\% performance of the models even when only 15\% of the original variables are present.
Learnability of Linear Thresholds from Label Proportions
This bound is tight for the non-monochromatic bags case. The above is in contrast to the usual supervised learning setup (i. e., unit-sized bags) in which LTFs are efficiently learnable to arbitrary accuracy using linear programming, and even a trivial algorithm (any LTF or its complement) achieves an accuracy of $1/2$.
