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DAGER: Exact Gradient Inversion for Large Language Models
However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive.
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback Marcel T orne 1,2 Max Balsells 3 Zihan Wang
This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
Empirically, we verify the computational efficiency of our methods.
Learning Diffusion Priors from Observations by Expectation Maximization
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems.
Explore to Generalize in Zero-Shot RL
Recent developments in reinforcement learning (RL) led to algorithms that surpass human experts in a broad range of tasks [Mnih et al., 2015, Vinyals et al., 2019, Schrittwieser et al., 2020, Wurman et al.,
MedJourney: Benchmark and Evaluation of Large Language Models over Patient Clinical Journey Xian Wu
In this paper, we address this gap by dividing a typical patient's clinical journey