Efficient number representation is essential for federated learning, natural language processing, and network measurement solutions. Due to timing, area, and power constraints, such applications use narrow bit-width (e.g., 8-bit) number systems. The widely used floating-point systems exhibit a trade-off between the counting range and accuracy. This paper introduces Floating-Floating-Point (F2P) - a floating point number that varies the partition between mantissa and exponent. Such flexibility leads to a large counting range combined with improved accuracy over a selected sub-range. Our evaluation demonstrates that moving to F2P from the state-of-the-art improves network measurement accuracy and federated learning.

Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning. In this paper, we improve on previous DME techniques that achieve the optimal $O(1/n)$ Normalized Mean Squared Error (NMSE) guarantee by asymptotically improving the complexity for either encoding or decoding (or both). To achieve this, we formalize the problem in a novel way that allows us to use off-the-shelf mathematical solvers to design the quantization.

In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. This process, known as uncertainty (or confidence) estimation, is particularly important in mission-critical applications such as autonomous driving. In this work, we put forward a novel geometric-based approach for improving uncertainty estimations in machine learning models. Our approach involves using the geometric distance of the current input from existing training inputs as a signal for estimating uncertainty, and then calibrating this signal using standard post-hoc techniques. We demonstrate that our method leads to more accurate uncertainty estimations than recently proposed approaches through extensive evaluation on a variety of datasets and models. Additionally, we optimize our approach so that it can be implemented on large datasets in near real-time applications, making it suitable for time-sensitive scenarios.

Uncertainty calibration is the process of adapting machine learning models' confidence estimations to be consistent with the actual success probability of the model [Guo et al., Machine learning classifiers are probabilistic in nature, 2017a]. The model's confidence evaluation on its classifications, and thus inevitably involve uncertainty. Predicting i.e., the model's prediction of the success ratio on a the probability of a specific input to be specific input, is an essential aspect of mission-critical machine correct is called uncertainty (or confidence) estimation learning applications as it provides a realistic estimate and is crucial for risk management. Posthoc of the classification's success probability and facilitates informed model calibrations can improve models' uncertainty decisions about the current situation. Even a very estimations without the need for retraining, accurate model may run into an unexpected situation, which and without changing the model.

Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate Figure 1: Example of the lack of robustness in a gradient a capability for verifying large models. Finally, we show boosted model trained over a traffic signs dataset. In the that some model configurations tend to be inherently more first row, an "80 km/h speed limit" sign is misclassified as robust than others.