Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models WSJ
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Separating Oblivious and Adaptive Differential Privacy under Continual Observation
arXiv:2603.11029v2 Announce Type: replace-cross Abstract: We resolve an open question of Jain, Raskhodnikova, Sivakumar, and Smith (ICML 2023) by exhibiting a problem separating differential privacy under continual observation in the oblivious and adaptive settings. The continual observation (a.k.a. continual release) model formalizes privacy for streaming algorithms, where data is received over time and output is released at each time step. In the oblivious setting, privacy need only hold for data streams fixed in advance; in the adaptive setting, privacy is required even for streams that can be chosen adaptively based on the streaming algorithm's output. We describe the first explicit separation between the oblivious and adaptive settings. The problem showing this separation is based on
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Traditional demand planning struggles with dynamic markets, relying too heavily on historical data. AI changes this by enabling real-time demand sensing, probabilistic forecasting, and reinforcement learning for smarter decisions. By combining data, predictive models, and digital twins, businesses can optimize inventory, reduce stockouts, and respond faster to disruptions—turning planning into a continuous, adaptive system. Read All
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Separating Oblivious and Adaptive Differential Privacy under Continual Observation
arXiv:2603.11029v2 Announce Type: replace-cross Abstract: We resolve an open question of Jain, Raskhodnikova, Sivakumar, and Smith (ICML 2023) by exhibiting a problem separating differential privacy under continual observation in the oblivious and adaptive settings. The continual observation (a.k.a. continual release) model formalizes privacy for streaming algorithms, where data is received over time and output is released at each time step. In the oblivious setting, privacy need only hold for data streams fixed in advance; in the adaptive setting, privacy is required even for streams that can be chosen adaptively based on the streaming algorithm's output. We describe the first explicit separation between the oblivious and adaptive settings. The problem showing this separation is based on
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