Consider N cooperative agents such that for T turns, each agent n takes an action an and receives a stochastic reward rn (a1,...,aN). Agents cannot observe the actions of other agents and do not know even their own reward function. The agents can communicate with their neighbors on a connected graph Gwith diameter d(G). We want each agent nto achieve an expected average reward of at least λn over time, for a given quality of service (QoS) vector λ. AQoS vector λis not necessarily achievable.

In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals.

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset. As the size of datasets contemporary machine learning models rely on becomes increasingly large, condensation methods become a prominent direction for accelerating network training and reducing data storage. Despite numerous methods have been proposed in this rapidly growing field, evaluating and comparing different condensation methods is non-trivial and still remains an open issue. The quality of condensed dataset are often shadowed by many critical contributing factors to the end performance, such as data augmentation and model architectures. The lack of a systematic way to evaluate and compare condensation methods not only hinders our understanding of existing techniques, but also discourages practical usage of the synthesized datasets. This work provides the first large-scale standardized benchmark on Dataset Condensation. It consists of a suite of evaluations to comprehensively reflect the generability and effectiveness of condensation methods through the lens of their generated dataset. Leveraging this benchmark, we conduct a large-scale study of current condensation methods, and report many insightful findings that open up new possibilities for future development. The benchmark library, including evaluators, baseline methods, and generated datasets, is open-sourced1 to facilitate future research and application.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their'right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for'forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP. At the same time, SCRUB is also consistently a top performer on metrics that measure model utility (i.e.

In addition to the four appendix sections mentioned in our main paper, we would like to draw atten-1 tion to two additional experiments: one evaluating the practical training and coding time, and the2 other investigating the impact of the number of training samples. These two experiments, especially3 the later one, offer crucial insights and are detailed in Appendix E1 and Appendix E2, respectively.4 Algorithm 1 A* encoding Require: Proposal distribution pw and target distribution qw. In our experiments, we used global-bound depth-limited A*7 coding to achieve this [1]. We describe the encoding procedure in Algorithm 1 and the decoding8 procedure in Algorithm 2. For brevity, we refer to this particular variant of the algorithm as A*9 coding for the rest of the appendix.10

Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. However, most current solutions for this are inefficient, as quantization to low-bit precision substantially degrades the reconstruction quality. To address this issue, we propose overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding instead of quantizing and entropy coding it. This strategy enables direct optimization of the rate-distortion performance by minimizing the β-ELBO, and target different rate-distortion trade-offs for a given network architecture by adjusting β. Moreover, we introduce an iterative algorithm for learning prior weight distributions and employ a progressive refinement process for the variational posterior that significantly enhances performance. Experiments show that our method achieves strong performance on image and audio compression while retaining simplicity.

As people increasingly rely on AI chatbots for guidance, even on financial matters, a healthy dose of skepticism is critical. I've used ChatGPT to help me build a budget before, and it was genuinely helpful. After I input my monthly salary as well as my standard utilities and recurring expenses, the chatbot drafted a few solid options, and I tweaked them into penny-pinching perfection. "Millions of people turn to ChatGPT with money-related questions, from understanding debt to building budgets and learning financial concepts," says Niko Felix, an OpenAI spokesperson, when reached for comment. "ChatGPT can be a helpful tool for exploring options, preparing questions, and making financial topics easier to understand, but it is not a substitute for licensed financial professionals." OpenAI's Terms of Use state that the AI tool is not meant to replace professional financial advice.

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Deepika Vemuri who is working on interpretability and concept-based learning. We found out more about the two aspects of concept-based models that she's been researching. Could you tell us a bit about your PhD - where are you studying, and what is the topic of your research? I'm a PhD student from IIT Hyderabad working with Dr Vineeth N Balasubramanian, supported by the PMRF Fellowship. Most current state-of-the-art models are black boxes, which is especially problematic when these models are used in high-stakes applications like criminal justice and healthcare, where people's lives depend on the decisions of these models.

Let qu(θ) set as Eq. For notational simplicity, let θ0 = θ(t 1). B.1 Hyperparameter settings Training In Table 2, we enumerate the hyperparameters used for our results in Section 5. Since we use expert trajectories for all methods to train the Bayesian pseudocoresets, we refer to hyperparameters related to expert trajectories, such as the number of SGD steps or the maximum random starting points, described in [8]. We found that a slightly shorter expert training step is better for BPC-fKL, so we used an expert step 1 epoch shorter than BPC-W. For each setting, we used the best learning rate from a hyperparameter sweep over {0.01,0.02,0.03,0.04}.