The target isavector in RNp, half comprised of "distance" units and half comprised of "direction" units. This calculation is performed in d dimensions. Next, we ran a much narrower sweep ofαE/αI ratio values in the range(2.5,10), Oneratio αE/αI = 3.5417appeared to outperform DoS, but this was due to a single lucky seed (Figure 1e).

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its $\textit{modality-aligned}$ setting, $\textit{i.e.}$, the audio and visual modality are $\textit{both}$ assumed to signal the prediction target.With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored $\textit{unaligned}$ setting, where the goal is to recognize audio and visual events in a video with only weak labels observed.Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both).To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed $\textbf{V}$isual-$\textbf{A}$udio $\textbf{L}$abel Elab$\textbf{or}$ation (VALOR), is innovated to harvest modality labels for the training events.Empirical studies show that the harvested labels significantly improve an attentional baseline by $\textbf{8.0}$ in average F-score (Type@AV).Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality.Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin ($\textbf{+5.4}$

Constraint-based causal discovery relies on numerous conditional independence tests (CITs), but its practical applicability is severely constrained by the prohibitive computational cost, especially as CITs themselves have high time complexity with respect to the sample size. To address this key bottleneck, we introduce the Ensemble Conditional Independence Test (E-CIT), a general and plug-and-play framework. E-CIT operates on an intuitive divide-and-aggregate strategy: it partitions the data into subsets, applies a given base CIT independently to each subset, and aggregates the resulting p-values using a novel method grounded in the properties of stable distributions. This framework reduces the computational complexity of a base CIT to linear in the sample size when the subset size is fixed. Moreover, our tailored p-value combination method offers theoretical consistency guarantees under mild conditions on the subtests. Experimental results demonstrate that E-CIT not only significantly reduces the computational burden of CITs and causal discovery but also achieves competitive performance. Notably, it exhibits an improvement in complex testing scenarios, particularly on real-world datasets.

The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wraps around any statistical inference procedure to safely enhance sample efficiency by combining synthetic and real data. Our framework leverages high-quality synthetic data to boost statistical power, yet adaptively defaults to the standard inference method using only real data when synthetic data is of low quality. The error of our method remains below a user-specified bound without any distributional assumptions on the synthetic data, and decreases as the quality of the synthetic data improves. This flexibility enables seamless integration with conformal prediction, risk control, hypothesis testing, and multiple testing procedures, all without modifying the base inference method. We demonstrate the benefits of our method on challenging tasks with limited labeled data, including AlphaFold protein structure prediction, and comparing large reasoning models on complex math problems.

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its \textit{modality-aligned} setting, \textit{i.e.}, the audio and visual modality are \textit{both} assumed to signal the prediction target.With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored \textit{unaligned} setting, where the goal is to recognize audio and visual events in a video with only weak labels observed.Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both).To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed \textbf{V} isual- \textbf{A} udio \textbf{L} abel Elab \textbf{or} ation (VALOR), is innovated to harvest modality labels for the training events.Empirical studies show that the harvested labels significantly improve an attentional baseline by \textbf{8.0} in average F-score (Type@AV).Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality.Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin ( \textbf{ 5.4} F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well.

Utilizing the theory of reproducing kernels, we reduce this hypothesis to a simple one-sided score test for a scalar parameter, develop a testing procedure that is robust against the misspecification of kernel functions, and also propose an ensemble-based estimator for the null model to guarantee test performance in small samples. To demonstrate the utility of the proposed method, we apply our test to the problem of detecting nonlinear interaction between groups of continuous features. We evaluate the finite-sample performance of our test under different data-generating functions and estimation strategies for the null model. Our results reveal interesting connections between notions in machine learning (model underfit/overfit) and those in statistical inference (i.e. Type I error/power of hypothesis test), and also highlight unexpected consequences of common model estimating strategies (e.g.

Hereby, we investigate over 100 hand-crafted and synthetically generated prompts and find that the Memorization in Large Language Models (LLMs) correct PII is extracted in less than 1% of cases. In has recently enjoyed a surge of interest (Hartmann contrast, using the true prefix of the target PII as et al., 2023) ranging from memorization localization a single query yields extraction rates of up to 6%. (Maini et al., 2023), quantification (Carlini Second, we propose PII-Compass, a novel method et al., 2022) to controlling (Ozdayi et al., 2023) and that achieves a substantially higher extraction rate auditing (Zhang et al., 2023a). The major reason than simple adversarial prompts. Our approach is for this is the risk of training data extraction (Carlini based on the intuition that querying the model with et al., 2021; Ishihara, 2023). To assess this risk, a prompt that has a close embedding to the embedding various methods have been proposed in prior work of the target piece of data, i.e., the PII and its (Yu et al., 2023; Zhang et al., 2023b; Panda et al., prefix, should increase the likelihood of extracting 2024; Wang et al., 2024). In this work, we aim to the PII. We do this by prepending the hand-crafted assess the privacy leakage risk of a subclass of training prompt with a true prefix of a different data subject data, namely personal identifiable information than the targeted data subject.

With the wide adoption of machine learning techniques, requirements have evolved beyond sheer high performance, often requiring models to be trustworthy. A common approach to increase the trustworthiness of such systems is to allow them to refrain from predicting. Such a framework is known as selective prediction. While selective prediction for classification tasks has been widely analyzed, the problem of selective regression is understudied. This paper presents a novel approach to selective regression that utilizes model-agnostic non-parametric uncertainty estimation. Our proposed framework showcases superior performance compared to state-of-the-art selective regressors, as demonstrated through comprehensive benchmarking on 69 datasets. Finally, we use explainable AI techniques to gain an understanding of the drivers behind selective regression. We implement our selective regression method in the open-source Python package doubt and release the code used to reproduce our experiments.