ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1 , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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A Graph Tsetlin Machine with hypervectorized macro multigraphs and message-passing clauses anticipates four USD/JPY regimes, reaching 70.7% overall OOS accuracy and beating reduced-graph and CoTM baselines on stagnant and choppy classes.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes: A Deep Logical Learning Approach using Graph Tsetlin Machines
A Graph Tsetlin Machine with hypervectorized macro multigraphs and message-passing clauses anticipates four USD/JPY regimes, reaching 70.7% overall OOS accuracy and beating reduced-graph and CoTM baselines on stagnant and choppy classes.