7 papers
Exact Unlearning via Quantized Sufficient Statistics
Ami Tavory, Shripad Gade, Tal Sarig +2
Exact unlearning requires a deployed predictor to match one rebuilt without the information named by a deletion request. Existing general-purpose exact methods localize retraining…
Bandits via Additive Quantized Representations
Ami Tavory, Noam Touitou, Tal Sarig +2
Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but require periodic retraining and…
Prequential E-Values for Selected-GP Near-Optimality Certificates
Ami Tavory, Noa Cohen
When optimizing an expensive black-box function sequentially, as in hyperparameter optimization, we may want to stop once the best evaluated value is certified within …
Fisher Simplicity in Kolmogorov-Arnold Networks and Multilayer Perceptrons
Ami Tavory, Meir Feder
Kolmogorov-Arnold Networks (KANs) are motivated in part by interpretability: their learned edge functions can be inspected, pruned, and reduced to symbolic structure. In a fixed-ba…
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
Guy Bar-Shalom, Ami Tavory, Itay Evron +3
Weight-space models learn directly from the parameters of neural networks, enabling tasks such as predicting their accuracy on new datasets. Naive methods -- like applying MLPs to…
Billion-Scale Graph Foundation Models
Maya Bechler-Speicher, Yoel Gottlieb, Andrey Isakov +5
Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, ext…
Determining Principal Component Cardinality through the Principle of Minimum Description Length
Ami Tavory
PCA (Principal Component Analysis) and its variants areubiquitous techniques for matrix dimension reduction and reduced-dimensionlatent-factor extraction. One significant challenge…