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Found 4 papers, 1 papers with code
New Loss Functions for Fast Maximum Inner Product Search
In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions.
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions.
Privacy and Utility Tradeoff in Approximate Differential Privacy
We show that the multiplicative gap of the lower bounds and upper bounds goes to zero in various high privacy regimes, proving the tightness of the lower and upper bounds and thus establishing the optimality of the truncated Laplacian mechanism.
Optimal Noise-Adding Mechanism in Additive Differential Privacy
We derive the optimal $(0, \delta)$-differentially private query-output independent noise-adding mechanism for single real-valued query function under a general cost-minimization framework.
