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Offline Reinforcement Learning with Differential Privacy Anonymous Author(s) Affiliation Address email
Differential privacy (DP) [Dwork et al., 2006] is a well-established definition of privacy with many
OfflineReinforcementLearningwithDifferential Privacy
Since offline RL does not require access to the environment, it can be applied to problems where interaction with environment is infeasible,e.g., when collecting new data is costly (trade or finance [Zhang et al., 2020]), risky (autonomous driving [Sallab et al., 2017]) or illegal / unethical (healthcare [Raghu etal.,2017]).
Cognitive Model Discovery via Disentangled RNNs
Computational cognitive models are a fundamental tool in behavioral neuroscience.
Federated Learning over Connected Modes Dennis Grinwald
In each communication round, each client uniformly samples points in the neighborhood of their projected point (top-right plot), and jointly train the solution simplex.
DreamCatcher: A Wearer-aware Sleep Event Dataset Based on Earables in Non-restrictive Environments
Widely available earbuds equipped with sensors (also known as earables) can be combined with a sleep event detection algorithm to offer a convenient alternative to laborious clinical tests for individuals suffering from sleep disorders. Although various solutions utilizing such devices have been proposed to detect sleep events, they ignore the fact that individuals often share sleeping spaces with roommates or couples. To address this issue, we introduce DreamCatcher, the first publicly available dataset for wearer-aware sleep event algorithm development on earables.
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
For example, we achieve a Wikitext2 perplexity of 5.95 on the LLaMA2-70B