GenAI4ELab/papercli-papers huggingface dataset:
Latent Graph Inference using Product Manifolds
Authors: Haitz Sáez de Ocáriz Borde; Anees Kazi; Federico Barbero; Pietro Lio
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows models to dynamically learn the intrinsic graph structure of problems where the connectivity patterns of data may not be directly accessible. In this work, we generalize the discrete Differentiable Graph Module (dDGM) for latent graph learning. The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated. By incorporating Riemannian geometry into the model and generating more complex embedding spaces, we can improve the performance of the latent graph inference system. In particular, we propose a computationally tractable approach to produce product manifolds of constant curvature model spaces that can encode latent features of varying structure. The latent representations mapped onto the inferred product manifold are used to compute richer similarity measures that are leveraged by the latent graph learning model to obtain optimized latent graphs. Moreover, the curvature of the product manifold is learned during training alongside the rest of the network parameters and based on the downstream task, rather than it being a static embedding space. Our novel approach is tested on a wide range of datasets, and outperforms the original dDGM model.
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Authors: Florent Delgrange; Ann Nowe; Guillermo Perez
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios is hindered by their lack of formal guarantees. Variational Markov Decision Processes (VAE-MDPs) are discrete latent space models that provide a reliable framework for distilling formally verifiable controllers from any RL policy. While the related guarantees address relevant practical aspects such as the satisfaction of performance and safety properties, the VAE approach suffers from several learning flaws (posterior collapse, slow learning speed, poor dynamics estimates), primarily due to the absence of abstraction and representation guarantees to support latent optimization. We introduce the Wasserstein auto-encoded MDP (WAE-MDP), a latent space model that fixes those issues by minimizing a penalized form of the optimal transport between the behaviors of the agent executing the original policy and the distilled policy, for which the formal guarantees apply. Our approach yields bisimulation guarantees while learning the distilled policy, allowing concrete optimization of the abstraction and representation model quality. Our experiments show that, besides distilling policies up to 10 times faster, the latent model quality is indeed better in general. Moreover, we present experiments from a simple time-to-failure verification algorithm on the latent space. The fact that our approach enables such simple verification techniques highlights its applicability.
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
Authors: Alexander Atanasov; Blake Bordelon; Sabarish Sainathan; Cengiz Pehlevan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size $P^*$, we empirically find the finite-width network generalization becomes worse than that of the infinite width network. In this work, we empirically study the transition from infinite-width behavior to this \textit{variance-limited} regime  as a function of sample size $P$ and network width $N$. We find that finite-size effects can become relevant for very small dataset sizes on the order of $P^* \sim \sqrt{N}$ for polynomial regression with ReLU networks. We discuss the source of these effects using an argument based on the variance of the NN's final neural tangent kernel (NTK). This transition can be pushed to larger $P$ by enhancing feature learning or by ensemble averaging the networks. We find that the learning curve for regression with the final NTK is an accurate approximation of the NN learning curve. Using this, we provide  a toy model which also exhibits $P^* \sim \sqrt{N}$ scaling and has $P$-dependent benefits from feature learning.
The Lie Derivative for Measuring Learned Equivariance
Authors: Nate Gruver; Marc Anton Finzi; Micah Goldblum; Andrew Gordon Wilson
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation equivariance directly encoded in their architecture. The rising success of vision transformers, which have no explicit architectural bias towards equivariance, challenges this narrative and suggests that augmentations and training data might also play a significant role in their performance. In order to better understand the role of equivariance in recent vision models, we apply the Lie derivative, a method for measuring equivariance with strong mathematical foundations and minimal hyperparameters. Using the Lie derivative, we study the equivariance properties of hundreds of pretrained models, spanning CNNs, transformers, and Mixer architectures. The scale of our analysis allows us to separate the impact of architecture from other factors like model size or training method. Surprisingly, we find that many violations of equivariance can be linked to spatial aliasing in ubiquitous network layers, such as pointwise non-linearities, and that as models get larger and more accurate they tend to display more equivariance, regardless of architecture. For example, transformers can be more equivariant than convolutional neural networks after training.
LAVA: Data Valuation without Pre-Specified Learning Algorithms
Authors: Hoang Anh Just; Feiyang Kang; Tianhao Wang; Yi Zeng; Myeongseob Ko; Ming Jin; Ruoxi Jia
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Traditionally, data valuation is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choices of the underlying learning algorithm. However, this dependence is undesirable for many use cases of data valuation, such as setting priorities over different data sources in a data acquisition process and informing pricing mechanisms in a data marketplace. In these scenarios, data needs to be valued before the actual analysis and the choice of the learning algorithm is still undetermined then. Another side-effect of the dependence is that to assess the value of individual points, one needs to re-run the learning algorithm with and without a point, which incurs a large computation burden. 

This work leapfrogs over the current limits of data valuation methods by introducing a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. Our main results are as follows. $\textbf{(1)}$ We develop a proxy for the validation performance associated with a training set based on a non-conventional $\textit{class-wise}$ $\textit{Wasserstein distance}$ between the training and the validation set. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions. $\textbf{(2)}$ We develop a novel method to value individual data based on the sensitivity analysis of the $\textit{class-wise}$ Wasserstein distance. Importantly, these values can be directly obtained $\textit{for free}$ from the output of off-the-shelf optimization solvers once the Wasserstein distance is computed. $\textbf{(3) }$We evaluate our new data valuation framework over various use cases related to detecting low-quality data
and show that, surprisingly, the learning-agnostic feature of our framework enables a $\textit{significant improvement}$ over the state-of-the-art performance while being $\textit{orders of magnitude faster.}$
Quantized Disentangled Representations for Object-Centric Visual Tasks
Authors: Daniil Kirilenko; Alexandr Korchemnyi; Alexey Kovalev; Aleksandr Panov
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recently, the pre-quantization of image features into discrete latent variables has helped to achieve remarkable results in image modeling. In this paper, we propose a method to learn discrete latent variables applied to object-centric tasks. In our approach, each object is assigned a slot which is represented as a vector generated by sampling from non-overlapping sets of low-dimensional discrete variables.
We empirically demonstrate that embeddings from the learned discrete latent spaces have the disentanglement property. The model is trained with a set prediction and object discovery as downstream tasks. It achieves the state-of-the-art results on the CLEVR dataset among a class of object-centric methods for set prediction task. We also demonstrate manipulation of individual objects in a scene with controllable image generation in the object discovery setting.
Data Pricing Mechanism Based on Property Rights Compensation Distribution
Authors: shiqian Liu; Peizheng Wang; Chao Wu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: While machine learning (ML) benefits from data, it also faces the challenges of ambiguous data ownership, including privacy violations and increased costs of using data. Yet existing approaches to data valuation may focus on preventing privacy breaches, but do not truly protect data ownership. This is because a data trading marketplace that protects data ownership should achieve this goal: once data is traded, its ownership does not transfer to a new owner but merely enlarges its coverage. Considering that the transfer of property rights in the process of data trading makes compensation necessary, this paper proposes the first data valuation mechanism based on modern property rights theory. Specifically, we propose the integration of property rights to improve the final revenue of the entire workflow called the “data chain” while compensating process executors who lost ownership after integration. Then, we consider the expectations of both the integrator and the integrated party during the compensation allocation. For the former, we apply compound interest to assess a total compensation equivalent to the time value for the Data chain. For the latter, we respect and meet their expectations as much as possible. To achieve this, we provide the framework based on Least-core to assign the compensation and prove that our framework can also work compared to existing algorithms. Finally, to cope with more complex situations, we adjust the traditional Least-core and demonstrate theoretically and experimentally that the compensation mechanism is feasible and effective in solving the data pricing problem.
Early Stopping for Deep Image Prior
Authors: Hengkang Wang; Taihui Li; Zhong Zhuang; Tiancong Chen; Hengyue Liang; Ju Sun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep image prior (DIP) and its variants have shown remarkable potential for solving inverse problems in computational imaging (CI), needing no separate training data. Practical DIP models are often substantially overparameterized. During the learning process, these models learn the desired visual content first and then pick up the potential modeling and observational noise, i.e., overfitting. Thus, the practicality of DIP hinges on early stopping (ES) that captures the transition period. In this regard, the majority of prior DIP works for CI tasks only demonstrate the potential of the models---reporting the peak performance against the groundtruth but providing no clue about how to operationally obtain near-peak performance without access to the groundtruth. In this paper, we set to break this practicality barrier of DIP, and propose an efficient ES strategy that consistently detects near-peak performance across several CI tasks and DIP variants. Simply based on the running variance of DIP intermediate reconstructions, our ES method not only outpaces the existing ones---which only work in very narrow regimes, but also remains effective when combined with methods that try to mitigate overfitting.
Non-parametric Outlier Synthesis
Authors: Leitian Tao; Xuefeng Du; Jerry Zhu; Yixuan Li
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Recent work on outlier synthesis modeled the feature space as parametric Gaussian distribution, a strong and restrictive assumption that might not hold in reality. In this paper, we propose a novel framework, non-parametric outlier synthesis (NPOS), which generates artificial OOD training data and facilitates learning a reliable decision boundary between ID and OOD data. Importantly, our proposed synthesis approach does not make any distributional assumption on the ID embeddings, thereby offering strong flexibility and generality. We show that our synthesis approach can be mathematically interpreted as a rejection sampling framework. Extensive experiments show that NPOS can achieve superior OOD detection performance, outperforming the competitive rivals by a significant margin. Code is publicly available at https://github.com/deeplearning-wisc/npos.
Learning to Generate Columns with Application to Vertex Coloring
Authors: Yuan Sun; Andreas T Ernst; Xiaodong Li; Jake Weiner
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present a new column generation approach based on Machine Learning (ML) for solving combinatorial optimization problems. The aim of our method is to generate high-quality columns that belong to an optimal integer solution, in contrast to the traditional approach that aims at solving linear programming relaxations. To achieve this aim, we design novel features to characterize a column, and develop an effective ML model to predict whether a column belongs to an optimal integer solution. We then use the ML model as a filter to select high-quality columns generated from a sampling method and use the selected columns to construct an integer solution.  Our method is computationally fast compared to the traditional methods that generate columns by repeatedly solving a pricing problem. We demonstrate the efficacy of our method on the vertex coloring problem, by empirically showing that the columns selected by our ML model are significantly better, in terms of the integer solution that can be constructed from them, than those selected randomly or based only on their reduced cost. Further, we show that the columns generated by our method can be used as a warm start to boost the performance of a column generation-based heuristic.
Batch Normalization Explained
Authors: Randall Balestriero; Richard Baraniuk
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A critically important, ubiquitous, and yet poorly understood ingredient in modern deep networks (DNs) is batch normalization (BN), which centers and normalizes the feature maps. To date, only limited progress has been made understanding why BN boosts DN learning and inference performance; work has focused exclusively on showing that BN smooths a DN's loss landscape. In this paper, we study BN theoretically from the perspective of function approximation; we exploit the fact that most of today's state-of-the-art DNs are continuous piecewise affine (CPA) splines that fit a predictor to the training data via affine mappings defined over a partition of the input space (the so-called ``linear regions''). We demonstrate that BN is an unsupervised learning technique that -- independent of the DN's weights or  gradient-based learning -- adapts the geometry of a DN's spline partition to match the data. BN provides a ``smart initialization'' that boosts the performance of DN learning, because it adapts even a DN initialized with random weights to align its spline partition with the data. We also show that the variation of BN statistics between mini-batches introduces a dropout-like random perturbation to the partition boundaries and hence the decision boundary for classification problems. This per mini-batch perturbation reduces overfitting and improves generalization by increasing the margin between the training samples and the decision boundary.
Leveraged Asymmetric Loss with Disambiguation for Multi-label Recognition with One-Positive Annotations
Authors: Jingyi Cui; Tao Huang; Hanyuan Hang; Yisen Wang; James Kwok
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In the problem of multi-label learning from single positive labels (SPL), we learn the potential multiple labels from one observable single positive annotation. Despite many efforts to solve this problem, an effective algorithm with sound theoretical understanding is still in need. In this paper,  we propose a novel loss function for the SPL problem, called leveraged asymmetric loss with disambiguation (LASD), where we introduce a pair of leverage parameters to address the severe negative-positive imbalance. From the theoretical perspective, we analyze the SPL problem, for the first time, from the perspective of risk consistency, which links the SPL loss with losses for ordinary multi-label classification. We prove the consistency of our proposed LASD loss to the cost-sensitive Hamming loss, which provides guidance to the empirical choice of our proposed leverage parameters. In experiments, we demonstrate the effectiveness of our proposed LASD loss function over other state-of-the-art methods and empirically verify our theoretical results.
Efficient Discovery of Dynamical Laws in Symbolic Form
Authors: Sören Becker; Michal Klein; Alexander Neitz; Giambattista Parascandolo; Niki Kilbertus
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from time-series data of a single observed solution trajectory of the ODE. Our method is efficiently scalable: after one-time pretraining on a large set of ODEs, we can infer the governing laws of a new observed solution in a few forward passes of the model. First, we generate and make available a large dataset of more than 3M ODEs together with more than 63M numerical solutions for different initial conditions that may serve as a useful benchmark for future work on machine learning for dynamical systems. Then we show that our model performs better or on par with existing methods in various test cases in terms of accurate symbolic recovery of the ODE, especially for more complex expressions. Reliably recovering the symbolic form of dynamical laws is important as it allows for further dissemination of the inferred dynamics as well as meaningful modifications for predictions under interventions.
Mosaic Representation Learning for Self-supervised Visual Pre-training
Authors: Zhaoqing Wang; Ziyu Chen; Yaqian Li; Yandong Guo; Jun Yu; Mingming Gong; Tongliang Liu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Self-supervised learning has achieved significant success in learning visual representations without the need for manual annotation. To obtain generalizable representations, a meticulously designed data augmentation strategy is one of the most crucial parts. Recently, multi-crop strategies utilizing a set of small crops as positive samples have been shown to learn spatially structured features. However, it overlooks the diverse contextual backgrounds, which reduces the variance of the input views and degenerates the performance. To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations. Specifically, we randomly sample numbers of small crops from different input images and compose them into a mosaic view, which is equivalent to introducing different background information for each small crop. Additionally, we further jitter the mosaic view to prevent memorizing the spatial locations of each crop. Along with optimization, our MosRep gradually extracts more discriminative features. Extensive experimental results demonstrate that our method improves the performance far greater than the multi-crop strategy on a series of downstream tasks, e.g., +7.4% and +4.9% than the multi-crop strategy on ImageNet-1K with 1% label and 10% label, respectively. Code is available at https://github.com/DerrickWang005/MosRep.git.
Learning rigid dynamics with face interaction graph networks
Authors: Kelsey R Allen; Yulia Rubanova; Tatiana Lopez-Guevara; William F Whitney; Alvaro Sanchez-Gonzalez; Peter Battaglia; Tobias Pfaff
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have been less effective and efficient on rigid-body physics, except with very simple shapes. Existing methods that model collisions through the meshes' nodes are often inaccurate because they struggle when collisions occur on faces far from nodes. Alternative approaches that represent the geometry densely with many particles are prohibitively expensive for complex shapes. Here we introduce the ``Face Interaction Graph Network'' (FIGNet) which extends beyond GNN-based methods, and computes interactions between mesh faces, rather than nodes. Compared to learned node- and particle-based methods, FIGNet is around 4x more accurate in simulating complex shape interactions, while also 8x more computationally efficient on sparse, rigid meshes. Moreover, FIGNet can learn frictional dynamics directly from real-world data, and can be more accurate than analytical solvers given modest amounts of training data. FIGNet represents a key step forward in one of the few remaining physical domains which have seen little competition from learned simulators, and offers allied fields such as robotics, graphics and mechanical design a new tool for simulation and model-based planning.