Most recent research hasaddressed thisby focusing onlocal interpretability, which explains a model's individual predictions (e.g., the role of each feature in a patient's diagnosis) [25, 30, 34, 38]. Twospecial cases areS = andS = D, which respectively correspond to the mean prediction f (x ) = E[f(X)] and the full model predictionfD(x) = f(x).

Data augmentation plays a key role in modern machine learning pipelines.

L for LM; D for diffusion), an LM-guided diffusion model that generates music audios of state-of-the-art quality meanwhile reducing 95.7% to 99.6% forward

Figure Broden 5], reproduced mission. Image Classification.Figure 3 (left) foreachneuronN.

We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. While previous work provide worst-case bounds for arbitrary convex functions, it is often the case that the function at hand belongs to a smaller class that enjoys faster rates. Concretely, we show that for functions exhibiting κ-growth around the optimum, i.e., f ( x) f (x