I cite from ReMixMatch figure caption: "Augmentation anchoring. We use the prediction for a weakly augmented image (green, middle) as the target for predictions on strong augmentations of the same image". This sounds to me as a summary of the presented work, and as such I consider it a special case of the ReMixMatch. Authors have discussed the differences between their work and ReMixMatch, mentioning that (1) "ReMixMatch don t use pseudo labeling", and (2) ReMixMatch uses sharpening of pseudolabels and weight annealing of the unlabeled data loss. However, in section 3.2.1 of ReMixMatch, it is stated that the guessed labels are used as targets (for strongly augmented images) using cross-entropy loss.

Additional Feedback: I consider this work as a new method in the context of semi-supervised learning and actively learning. Indeed, these are the two topics the authors of the paper reviewed as the related work to this work. The method essentially is yet another way to rearrange labeled samples and unlabeled samples in order to identify "active" samples to improve the learning accuracy. Thus, it is not an eye-opening, truly novel approach. I would argue that this method is incrementally novel at best.

Selecting appropriate training data is crucial for effective instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across a diverse range of tasks. Influence-based methods show promise in achieving (1) by estimating the contribution of each training example to the model's predictions, but often struggle with (2). Our systematic investigation reveals that this underperformance can be attributed to an inherent bias where certain tasks intrinsically have greater influence than others. As a result, data selection is often biased towards these tasks, not only hurting the model's performance on others but also, counterintuitively, harms performance on these high-influence tasks themselves. As a remedy, we propose BIDS, a Balanced and Influential Data Selection algorithm. BIDS first normalizes influence scores of the training data, and then iteratively balances data selection by choosing the training example with the highest influence on the most underrepresented task. Experiments with both Llama-3 and Mistral-v0.3 on seven benchmarks spanning five diverse capabilities show that BIDS consistently outperforms both state-of-the-art influence-based algorithms and other non-influence-based selection frameworks. Surprisingly, training on a 15% subset selected by BIDS can even outperform full-dataset training with a much more balanced performance. Our analysis further highlights the importance of both instance-level normalization and iterative optimization of selected data for balanced learning of diverse capabilities.

Self-supervised learning holds the promise to learn good representations from real-world continuous uncurated data streams. However, most existing works in visual self-supervised learning focus on static images or artificial data streams. Towards exploring a more realistic learning substrate, we investigate streaming self-supervised learning from long-form real-world egocentric video streams. Inspired by the event segmentation mechanism in human perception and memory, we propose "Memory Storyboard" that groups recent past frames into temporal segments for more effective summarization of the past visual streams for memory replay. To accommodate efficient temporal segmentation, we propose a two-tier memory hierarchy: the recent past is stored in a short-term memory, and the storyboard temporal segments are then transferred to a long-term memory. Experiments on real-world egocentric video datasets including SAYCam and KrishnaCam show that contrastive learning objectives on top of storyboard frames result in semantically meaningful representations which outperform those produced by state-of-the-art unsupervised continual learning methods.

This paper is very interesting in general, and I believe that it has met the standard of NIPS poster. In particular, to the best of my knowledge, this is the first paper considering contextual combinatorial semi-bandits with *unknown* weights. However, I think some parts of the paper can still be improved, and will appreciate it if the authors polish the final version of the paper accordingly: 1) In Theorem 2: the O(T {2/3}) regret bound is somewhat unsatisfactory since I am expecting an O(T {1/2}) regret bound. If the authors believe that the O(T {2/3}) regret bound is intrinsic, please discuss. If the authors believe that it is due to unsatisfactory analysis, please also discuss (i.e. which step of the analysis leads to this non-tight regret bound). Please rewrite the motivation and explanation of the algorithm.

I found this paper interesting and well-written, but I have some significant questions and comments about the approach. The paper argues that sampling is useful because we can find the C most frequently sampled programs and show them to a user. As shown in Figure 6, there is more likely to be a correct program in the top 3 programs than in the top 1. But if we want to show the top C programs, do we really need to perform sampling, which the paper says is complicated by the existence of many long and unlikely programs that match the training examples? Why can't we simply find the MDL program and then run the solver again with length restrictions to find other consistent programs of the same length, or slightly longer lengths?

It treats an incredibly important and foundational problem (fairness), proposes a creative but simple new definition, gives techniques for achieving the definition, proves theorems with regards to optimality, and even provides empirical results. As learning algorithms are used more and more broadly in situations where their decisions affect people's lives, fairness of these algorithms becomes a critical technical, social, and legal problem. While there is certainly no single "right" definition and paradigm when it comes to fairness, this definition seems to clearly be *a* right definition. It's so clean and simple that in retrospect, it seems obvious--a sign of an excellent idea. One of the many things I love about this definition and this work is how it shifts the structure of power and incentives--once a learner is constrained to be fair, under either of the definitions proposed, she is immediately incentivised to gather more data or make other efforts to do a better job of understanding protected populations.

The results presented in the paper are impressive and significant enough. However, the results are quite empirical, non-conclusive, and lack of theoretical justification. For rebuttal, please focus on answering the (*), (**), and (***) mentioned in the following paragraphs. Reviewer is willing to change score if all the questions are well addressed. Novelty: The techniques proposed in the paper is novel in general. However, the proposed technique "feature matching" when training GAN has been explored to some extent: -- Generating Images with Perceptual Similarity Metrics based on Deep Networks by Dosovitskiy and Brox -- Autoencoding beyond pixels using a learned similarity metric by Larsen et al.

In my view, this is a beautiful paper that will advance the field of structured prediction significantly and provides a platform for further development. Nevertheless, the paper should be better related to existing work on vector-valued regression for structured output. A recent related work is but there are others: C eline Brouard, Florence D'Alch e-Buc, Marie Szafranski. The paper is generally well written, I have only few remarks: - line 70-72: you might note already here that this amounts to a ridge regression problem in the output Hilbert space. Good to mention it already here.

The technical results appear to be correct and the experimental results (which I think are quite preliminary) suggest the minimax SVM might be a good idea. I think the idea of robust Bayes decision rules makes sense and the authors show how under squared loss a connection to the Huber loss emerges. My main comment is that the paper itself is a somewhat difficult read due to terseness at key places, which might limit the impact of the paper. So, the rest of my comments are just geared towards improving the clarity of the paper. Technically, in every instance where the authors apply Danskin's theorem, it was not really clear what form of Danskin's theorem was being used, and therefore it was difficult to follow the derivation.