Discriminative Feature Feedback (DFF) (Dasgupta et al., 2018) is an interactive learning protocol in which a learner attempts to predict labels based on online feedback about previous correct labels, as well as discriminative features. Recent work (Bar Oz et al., 2025) studied DFF learning with general teacher classes and defined a dimension that characterizes the optimal mistake bound for deterministic algorithms in the realizable setting. In this work, we show that in sharp contrast to Online Learning, in DFF there can be an unbounded ratio between the optimal mistake bound of deterministic algorithms and that of randomized algorithms, even in the realizable setting. We further show that in this case, also non-realizable learning can have a mistake bound that does not depend on the dimension at all. This result relies on a new algorithmic technique that allows introducing new candidate hypotheses incrementally and could be of independent interest. We further show that in DFF, there can be a significant difference between the obtainable mistake bounds against an oblivious adversary and against an adaptive adversary, again in contrast to Online Learning. Our work shows that once richer feedback than labels is allowed, the landscape of randomized versus deterministic algorithms becomes significantly more involved, and raises new questions on characterizing the optimal mistake bound under differing randomization regimes.

We provide the first $\widetilde{\mathcal{O}}(d)$-sample algorithm for sampling from unbounded Gaussian distributions under the constraint of $(\varepsilon, \delta)$-differential privacy. This is a quadratic improvement over previous results for the same problem, settling an open question of Ghazi, Hu, Kumar, and Manurangsi.

Automated audio captioning is a task that generates textual descriptions for audio content, and recent studies have explored using visual information to enhance captioning quality. However, current methods often fail to effectively fuse audio and visual data, missing important semantic cues from each modality. To address this, we introduce LAVCap, a large language model (LLM)-based audio-visual captioning framework that effectively integrates visual information with audio to improve audio captioning performance. LAVCap employs an optimal transport-based alignment loss to bridge the modality gap between audio and visual features, enabling more effective semantic extraction. Additionally, we propose an optimal transport attention module that enhances audio-visual fusion using an optimal transport assignment map. Combined with the optimal training strategy, experimental results demonstrate that each component of our framework is effective. LAVCap outperforms existing state-of-the-art methods on the AudioCaps dataset, without relying on large datasets or post-processing. Code is available at https://github.com/NAVER-INTEL-Co-Lab/gaudi-lavcap.