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Found 7 papers, 0 papers with code
Speech-driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference
Finally, we employ the diffusion model to train and infer various gestures.
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries
One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010).
The Cost of Parallelizing Boosting
Karbasi and Larsen showed that "significant" parallelization must incur exponential blow-up: Any boosting algorithm either interacts with the weak learner for $\Omega(1 / \gamma)$ rounds or incurs an $\exp(d / \gamma)$ blow-up in the complexity of training, where $d$ is the VC dimension of the hypothesis class.
Hot PATE: Private Aggregation of Distributions for Diverse Task
Until now, PATE has primarily been explored with classification-like tasks, where each example possesses a ground-truth label, and knowledge is transferred to the student by labeling public examples.
Tight Time-Space Lower Bounds for Constant-Pass Learning
In this work, for any constant $q$, we prove tight memory-sample lower bounds for any parity learning algorithm that makes $q$ passes over the stream of samples.
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization
The problem of learning threshold functions is a fundamental one in machine learning.
On the Robustness of CountSketch to Adaptive Inputs
CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements.
