ICLR 2023 论文列表

Deep learning methods nowadays rely on massive data, resulting in substantial costs of data storage and model training. Data selection is a useful tool to alleviate such costs, where a coreset of massive data is extracted to practically perform on par with full data. Based on carefully-designed score criteria, existing methods first count the score of each data point and then select the data points whose scores lie in a certain range to construct a coreset. These methods work well in their respective preconceived scenarios but are not robust to the change of scenarios, since the optimal range of scores varies as the scenario changes. The issue limits the application of these methods, because realistic scenarios often mismatch preconceived ones, and it is inconvenient or unfeasible to tune the criteria and methods accordingly. In this paper, to address the issue, a concept of the moderate coreset is discussed. Specifically, given any score criterion of data selection, different scenarios prefer data points with scores in different intervals. As the score median is a proxy of the score distribution in statistics, the data points with scores close to the score median can be seen as a proxy of full data and generalize different scenarios, which are used to construct the moderate coreset. As a proof-of-concept, a universal method that inherits the moderate coreset and uses the distance of a data point to its class center as the score criterion, is proposed to meet complex realistic scenarios. Extensive experiments confirm the advance of our method over prior state-of-the-art methods, leading to a strong baseline for future research. The implementation is available at https://github.com/tmllab/Moderate-DS.

Recently, 2D semantic segmentation has witnessed a significant advancement thanks to the huge amount of 2D image datasets available. Therefore, in this work, we propose the first 2D-to-3D knowledge distillation strategy to enhance 3D semantic segmentation model with knowledge embedded in the latent space of powerful 2D models. Specifically, unlike standard knowledge distillation, where teacher and student models take the same data as input, we use 2D panoramas properly aligned with corresponding 3D rooms to train the teacher network and use the learned knowledge from 2D teacher to guide 3D student. To facilitate our research, we create a large-scale, fine-annotated 3D semantic segmentation benchmark, containing voxel-wise semantic labels and aligned panoramas of 5175 scenes. Based on this benchmark, we propose a 3D volumetric semantic segmentation network, which adapts Video Swin Transformer as backbone and introduces a skip connected linear decoder. Achieving a state-of-the-art performance, our 3D Segmenter is computationally efficient and only requires $3.8\%$ of the parameters compared to the prior art. Our code and data will be released upon acceptance.

Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include graph sparsification that samples a subgraph to reduce the amount of data aggregation and model sparsification that prunes the neural network to reduce the number of trainable weights. Despite the empirical successes in reducing the training cost while maintaining the test accuracy, the theoretical generalization analysis of sparse learning for GNNs remains elusive. To the best of our knowledge, this paper provides the first theoretical characterization of joint edge-model sparse learning from the perspective of sample complexity and convergence rate in achieving zero generalization error. It proves analytically that both sampling important nodes and pruning neurons with lowest-magnitude can reduce the sample complexity and improve convergence without compromising the test accuracy. Although the analysis is centered on two-layer GNNs with structural constraints on data, the insights are applicable to more general setups and justified by both synthetic and practical citation datasets.

Despite the clear performance benefits of data augmentations, little is known about why they are so effective. In this paper, we disentangle several key mechanisms through which data augmentations operate. Establishing an exchange rate between augmented and additional real data, we find that in out-of-distribution testing scenarios, augmentations which yield samples that are diverse, but inconsistent with the data distribution can be even more valuable than additional training data. Moreover, we find that data augmentations which encourage invariances can be more valuable than invariance alone, especially on small and medium sized training sets. Following this observation, we show that augmentations induce additional stochasticity during training, effectively flattening the loss landscape.

Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities). One key challenge is to scale PCs to model large and high-dimensional real-world datasets: we observe that as the number of parameters in PCs increases, their performance immediately plateaus. This phenomenon suggests that the existing optimizers fail to exploit the full expressive power of large PCs. We propose to overcome such bottleneck by latent variable distillation: we leverage the less tractable but more expressive deep generative models to provide extra supervision over the latent variables of PCs. Specifically, we extract information from Transformer-based generative models to assign values to latent variables of PCs, providing guidance to PC optimizers. Experiments on both image and language modeling benchmarks (e.g., ImageNet and WikiText-2) show that latent variable distillation substantially boosts the performance of large PCs compared to their counterparts without latent variable distillation. In particular, on the image modeling benchmarks, PCs achieve competitive performance against some of the widely-used deep generative models, including variational autoencoders and flow-based models, opening up new avenues for tractable generative modeling. Our code can be found at https://github.com/UCLA-StarAI/LVD.

Despite the recent advances in communication-efficient distributed bandit learning, most existing solutions are restricted to parametric models, e.g., linear bandits and generalized linear bandits (GLB). In comparison, kernel bandits, which search for non-parametric functions in a reproducing kernel Hilbert space (RKHS), offer higher modeling capacity. But the only existing work in distributed kernel bandits adopts a synchronous communication protocol, which greatly limits its practical use (e.g., every synchronization step requires all clients to participate and wait for data exchange). In this paper, in order to improve the robustness against delays and unavailability of clients that are common in practice, we propose the first asynchronous solution based on approximated kernel regression for distributed kernel bandit learning. A set of effective treatments are developed to ensure approximation quality and communication efficiency. Rigorous theoretical analysis about the regret and communication cost is provided; and extensive empirical evaluations demonstrate the effectiveness of our solution.