In this paper we study the uncapacitated facility location problem in the model of differential privacy (DP) with uniform facility cost.

We thank the reviewers for their detailed and constructive comments. Overall, the reviewers were positive about this contribution and liked the submission: "I generally The task is compelling and the benchmark is well thought out." The reviewers also raised concerns, which we will address next. For example, in CLEVR it now seems likely that some models (e.g., Relation Networks) have found shortcut "cheats" It is difficult to characterize what constitutes "intrinsic" difficulty, but by As a whole, the community must "go for recall" since By releasing PHYRE to the public, we hope to see rapid exploration of these good suggestions. We will attempt to improve the clarity.

Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances in linear mode connectivity -- identifying a linearly connected low-loss region in the parameter space of neural networks, which we call solution simplex.

Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

In this paper, we present a simple optimization-based preprocessing technique called Weight Magnitude Reduction (MagR) to improve the performance of posttraining quantization.

Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy. Furthermore, recreating existing figures that are not stored in formats preserving semantic information is equally complex. To tackle this problem, we introduce DeTikZify, a novel multimodal language model that automatically synthesizes scientific figures as semantics-preserving TikZ graphics programs based on sketches and existing figures.

We use diffusion models to generate visual spectrograms (second row) that look like natural images, which we call images that sound. These spectrograms can be converted into natural sounds (third row) using a pretrained vocoder, or colorized to obtain more visually pleasing results (first row). Please refer to our website to listen to the sounds.

Modelling the dynamics of multi-agent learning has long been an important research topic, but all of the previous works focus on 2-agent settings and mostly use evolutionary game theoretic approaches. In this paper, we study an n-agent setting with n tends to infinity, such that agents learn their policies concurrently over repeated symmetric bimatrix games with some other agents. Using the mean field theory, we approximate the effects of other agents on a single agent by an averaged effect. A Fokker-Planck equation that describes the evolution of the probability distribution of Q-values in the agent population is derived. To the best of our knowledge, this is the first time to show the Q-learning dynamics under an n-agent setting can be described by a system of only three equations.

The theoretical contributions of our work and the works [Mguni et al., AAAI'18; Mguni et al., AAMAS'19] mentioned

Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept values, affecting the downstream output. In this work, we introduce a method to perform such concept-based interventions on pretrained neural networks, which are not interpretable by design, only given a small validation set with concept labels. Furthermore, we formalise the notion of intervenability as a measure of the effectiveness of concept-based interventions and leverage this definition to fine-tune black boxes. Empirically, we explore the intervenability of black-box classifiers on synthetic tabular and natural image benchmarks. We focus on backbone architectures of varying complexity, from simple, fully connected neural nets to Stable Diffusion. We demonstrate that the proposed fine-tuning improves intervention effectiveness and often yields better-calibrated predictions. To showcase the practical utility of our techniques, we apply them to deep chest X-ray classifiers and show that fine-tuned black boxes are more intervenable than CBMs. Lastly, we establish that our methods are still effective under vision-languagemodel-based concept annotations, alleviating the need for a human-annotated validation set.