GenAI4ELab/papercli-papers huggingface dataset:
Scenario-based Question Answering with Interacting Contextual Properties
Authors: Haitian Sun; William W. Cohen; Ruslan Salakhutdinov
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In the scenario-based Question Answering (QA) task, models are asked to find answers that are appropriate to the user scenarios associated with the question and identify information that is missing from the scenarios but is necessary for the answers to hold. Scenarios commonly include multiple properties of users, such as age, employment status, and income level for the question “How much can I claim from this benefit”. The properties relevant to a potential answer are given in a document, which will state conditions necessary for the answer to hold. Documents also may specify how conditions interact with each other, e.g. with text like “one of the conditions below must apply”. Although understanding the relationship between conditions is crucial for solving this challenging QA task, limited work has been done so far in modeling this. In this paper, we propose the T-Reasoner model, which solves this problem with three jointly learned modules: an entailment module which checks whether a condition has been satisfied by the scenario, a decoding module which locates eligible answers from documents, and a reasoning module which infers the relationship between conditions and performs a reasoning step to determine the logically consistent answers and identify missing conditions. T-Reasoner outperforms strong baselines on a synthetic scenario-based QA dataset and achieves a new state-of-the-art on two scenario-based QA benchmarks, outperforming the prior best models by 3-10 points.
WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations
Authors: Tribhuvanesh Orekondy; Pratik Kumar; Shreya Kadambi; Hao Ye; Joseph Soriaga; Arash Behboodi
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this paper, we work towards a neural surrogate to model wireless electro-magnetic propagation effects in indoor environments.
Such neural surrogates provide a fast, differentiable, and continuous representation of the environment and enables end-to-end optimization for downstream tasks (e.g., network planning). Specifically, the goal of the paper is to render the wireless signal (e.g., time-of-flights, power of each path) in an environment as a function of the sensor's spatial configuration (e.g., placement of transmit and receive antennas). NeRF-based approaches have shown promising results in the visual setting (RGB image signal, with a camera sensor), where the key idea is to algorithmically evaluate the 'global' signal (e.g., using volumetric rendering) by breaking it down in a sequence of 'local' evaluations (e.g., using co-ordinate neural networks). In a similar spirit, we model the time-angle channel impulse response (the global wireless signal) as a superposition of multiple paths. The wireless characteristics (e.g., power) of each path is a result of multiple evaluations of a neural network that learns implicit ray-surface interaction properties. We evaluate our approach in multiple indoor scenarios and demonstrate that our model achieves strong performance (e.g., $<$0.33ns error in time-of-flight predictions). Furthermore, we demonstrate that our neural surrogate whitens the `black-box' wireless simulators, and thus enables inverse rendering applications (e.g., user localization).
Distributed Least Square Ranking with Random Features
Authors: Rong Yin; Yong Liu; Weiping Wang; Dan Meng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we study the statistical properties of pairwise ranking using distributed learning and random features (called DRank-RF) and establish its convergence analysis in probability. Theoretical analysis shows that DRank-RF remarkably reduces the computational requirements while preserving a satisfactory convergence rate. An extensive experiment verifies the effectiveness of DRank-RF. Furthermore, to improve the learning performance of DRank-RF, we propose an effective communication strategy for it and demonstrate the power of communications via theoretical assessments and numerical experiments.
On the Robustness of Dataset Inference
Authors: Sebastian Szyller; Rui Zhang; Jian Liu; N Asokan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning (ML) models are costly to train as they can require a significant amount of data, computational resources and technical expertise. Thus, they constitute valuable intellectual property that needs protection from adversaries wanting to steal them. $\textit{Ownership verification}$ techniques allow the victims of model stealing attacks to demonstrate that a suspect model was in fact stolen from theirs.
Although a number of ownership verification techniques based on watermarking or fingerprinting have been proposed, most of them fall short either in terms of security guarantees (well-equipped adversaries can evade verification) or computational cost. A fingerprinting technique introduced at ICLR '21, $\textit{Dataset Inference}$ (DI), has been shown to offer better robustness and efficiency than prior methods.
The authors of DI provided a correctness proof for linear (suspect) models. However, in the same setting, we prove that DI suffers from high false positives (FPs) -- it can incorrectly identify an independent model trained with non-overlapping data from the same distribution as stolen. We further prove that DI also triggers FPs in realistic, non-linear suspect models. We then confirm empirically that DI leads to FPs, with high confidence.
Second, we show that DI also suffers from false negatives (FNs) -- an adversary can fool DI by regularising a stolen model's decision boundaries using adversarial training, thereby leading to an FN. To this end, we demonstrate that DI fails to identify a model adversarially trained from a stolen dataset -- the setting where DI is the hardest to evade.
Finally, we discuss the implications of our findings, the viability of fingerprinting-based ownership verification in general, and suggest directions for future work.
ReD-GCN: Revisit the Depth of Graph Convolutional Network
Authors: Yuchen Yan; Yuzhong Chen; Mahashweta Das; Hao Yang; Hanghang Tong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Finding the proper depth $d$ of a GNN that provides strong representation power has drawn significant attention, yet nonetheless  largely  remains an open problem for the graph learning community. Although noteworthy progress has been made, the depth or the number of layers of a corresponding GCN is realized by a series of graph convolution operations, which naturally makes $d$ a positive integer ($d \in \mathbb{N}+$). An interesting question is whether breaking the constraint of $\mathbb{N}+$ by making $d$ a real number ($d \in \mathbb{R}$) can bring new insights into graph learning mechanisms. In this work, by redefining GCN's depth $d$ as a trainable parameter continuously adjustable within $(-\infty,+\infty)$, we open a new door of controlling its expressiveness on graph signal processing to model graph homophily/heterophily (nodes with similar/dissimilar labels/attributes tend to inter-connect). A simple and powerful GCN model ReD-GCN, is proposed to retain the simplicity of GCN and meanwhile automatically search for the optimal $d$ without the prior knowledge regarding whether the input graph is homophilic or heterophilic. Negative-valued $d$ intrinsically enables high-pass frequency filtering functionality for graph heterophily. Variants extending the model flexibility/scalability are also developed. The theoretical feasibility of having a real-valued depth with explainable physical meanings is ensured via eigen-decomposition of the graph Laplacian and a properly designed transformation function from the perspective of functional calculus. Extensive experiments demonstrate the superiority of ReD-GCN on node classification tasks for a variety of graphs. Furthermore, by introducing the concept of eigengraph, a novel graph augmentation method is obtained: the optimal $d$ effectively generates a new topology through a properly weighted combination of eigengraphs, which dramatically boosts the performance even for a vanilla GCN.
Zero-Label Prompt Selection
Authors: Chonghua Liao; Yanan Zheng; Zhilin Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label Prompt Selection (ZPS) method that selects prompts without any labeled data or gradient update. Specifically, given the candidate human-written prompts for a task, ZPS labels a set of unlabeled data with a prompt ensemble and uses the pseudo-labels for prompt selection. Experiments show that ZPS improves over prior methods by a sizeable margin in zero-label performance. We also extend ZPS to a few-shot setting and show its advantages over strong baselines such as prompt tuning and model tuning.
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities
Authors: Takashi Matsubara; Takaharu Yaguchi
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Many real-world dynamical systems are associated with first integrals (a.k.a. invariant quantities), which are quantities that remain unchanged over time. The discovery and understanding of first integrals are fundamental and important topics both in the natural sciences and in industrial applications. First integrals arise from the conservation laws of system energy, momentum, and mass, and from constraints on states; these are typically related to specific geometric structures of the governing equations. Existing neural networks designed to ensure such first integrals have shown excellent accuracy in modeling from data. However, these models incorporate the underlying structures, and in most situations where neural networks learn unknown systems, these structures are also unknown. This limitation needs to be overcome for scientific discovery and modeling of unknown systems. To this end, we propose first integral-preserving neural differential equation (FINDE). By leveraging the projection method and the discrete gradient method, FINDE finds and preserves first integrals from data, even in the absence of prior knowledge about underlying structures. Experimental results demonstrate that FINDE can predict future states of target systems much longer and find various quantities consistent with well-known first integrals in a unified manner.
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects
Authors: Ziyang Tang; Yiheng Duan; Stephanie Zhang; Lihong Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Randomized experiments (a.k.a. A/B tests) are a powerful tool for estimating treatment effects, to inform decisions making in business, healthcare and other applications. In many problems, the treatment has a lasting effect that evolves over time. A limitation with randomized experiments is that they do not easily extend to measure long-term effects, since running long experiments is time-consuming and expensive. In this paper, we take a reinforcement learning (RL) approach that estimates the average reward in a Markov process. Motivated by real-world scenarios where the observed state transition is nonstationary, we develop a new algorithm for a class of nonstationary problems, and demonstrate promising results in two synthetic datasets and one online store dataset.
Isometric Representations in Neural Networks Improve Robustness
Authors: Kosio Beshkov; Jonas Verhellen; Mikkel Elle Lepperød
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Artificial and biological agents are unable to learn given completely random and unstructured data. The structure of data is encoded in the distance or similarity relationships between data points. In the context of neural networks, the neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the structure in the data in a truthful manner, such representations should reflect the input distances and thus be continuous and isometric. Supporting this statement, recent findings in neuroscience propose that generalization and robustness are tied to neural representations being continuously differentiable. However, in machine learning, most algorithms lack robustness and are generally thought to rely on aspects of the data that differ from those that humans use, as is commonly seen in  adversarial attacks.

During cross-entropy classification,  the metric and structural properties of network representations are usually broken both between and within classes. This side effect from training can lead to instabilities under perturbations near locations where such structure is not preserved. One of the standard solutions to obtain robustness is to train specifically by introducing perturbations in the training data. This leads to networks that are particularly robust to specific training perturbations but not necessarily to general perturbations. While adding ad hoc regularization terms to improve robustness has become common practice, to our knowledge, forcing representations to preserve the metric structure of the input data as a stabilising mechanism has not yet been introduced.

In this work, we train neural networks to perform classification while simultaneously  maintaining the metric structure within each class, leading to continuous and isometric within-class representations. We show that such network representations turn out to be a beneficial component for making accurate and robust inferences about the world. By stacking layers with this property we provide the community with an network architecture that facilitates hierarchical manipulation of internal neural representations. Finally, we verify that our isometric regularization term improves the robustness to adversarial attacks on MNIST.
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Authors: Oleg Platonov; Denis Kuznedelev; Michael Diskin; Artem Babenko; Liudmila Prokhorenkova
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Node classification is a classical graph representation learning task on which Graph Neural Networks (GNNs) have recently achieved strong results. However, it is often believed that standard GNNs only work well for homophilous graphs, i.e., graphs where edges tend to connect nodes of the same class. Graphs without this property are called heterophilous, and it is typically assumed that specialized methods are required to achieve strong performance on such graphs. In this work, we challenge this assumption. First, we show that the standard datasets used for evaluating heterophily-specific models have serious drawbacks, making results obtained by using them unreliable. The most significant of these drawbacks is the presence of a large number of duplicate nodes in the datasets Squirrel and Chameleon, which leads to train-test data leakage. We show that removing duplicate nodes strongly affects GNN performance on these datasets. Then, we propose a set of heterophilous graphs of varying properties that we believe can serve as a better benchmark for evaluating the performance of GNNs under heterophily. We show that standard GNNs achieve strong results on these heterophilous graphs, almost always outperforming specialized models. Our datasets and the code for reproducing our experiments are available at https://github.com/yandex-research/heterophilous-graphs
Variational Reparametrized Policy Learning with Differentiable Physics
Authors: Zhiao Huang; Litian Liang; Zhan Ling; Xuanlin Li; Chuang Gan; Hao Su
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the problem of policy parameterization for reinforcement learning (RL) with high-dimensional continuous action space. Our goal is to find a good way to parameterize the policy of continuous RL as a multi-modality distribution. To this end, we propose to treat the continuous RL policy as a generative model over the distribution of optimal trajectories. We use a diffusion process-like strategy to model the policy and derive a novel variational bound which is the optimization objective to learn the policy. To maximize the objective by gradient descent, we introduce the Reparameterized Policy Gradient Theorem. This theorem elegantly connects classical method REINFORCE and trajectory return optimization for computing the gradient of a policy. Moreover, our method enjoys strong exploration ability due to the multi-modality policy parameterization; notably, when a strong differentiable world model presents, our method also enjoys the fast convergence speed of trajectory optimization. We evaluate our method on numerical problems and manipulation tasks within a differentiable simulator. Qualitative results show its ability to capture the multi-modality distribution of optimal trajectories, and quantitative results show that it can avoid local optima and outperforms baseline approaches.
Data Subset Selection via Machine Teaching
Authors: Stephen Mussmann; Alex Fang; Ludwig Schmidt; Kevin Jamieson
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the problem of data subset selection: given a fully labeled dataset and a training procedure, select a subset such that training on that subset yields approximately the same test performance as training on the full dataset. We propose an algorithm, inspired by recent work in machine teaching, that has theoretical guarantees, compelling empirical performance, and is model-agnostic meaning the algorithm's only information comes from the predictions of models trained on subsets. Furthermore, we prove lower bounds that show that our algorithm achieves a subset with near-optimal size (under computational hardness assumptions) while training on a number of subsets that is optimal up to extraneous log factors. We then empirically compare our algorithm, machine teaching algorithms, and coreset techniques on six common image datasets with convolutional neural networks. We find that our machine teaching algorithm can find a subset of CIFAR10 of size less than 16k that yields the same performance (5-6% error) as training on the full dataset of size 50k.
On the Saturation Effect of Kernel Ridge Regression
Authors: Yicheng Li; Haobo Zhang; Qian Lin
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The saturation effect refers to the  phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect  has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this paper, we provide a proof of this long-standing conjecture.
Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning
Authors: Shijie Zhu; Hui Zhao; Pengjie Wang; Hongbo Deng; Jian Xu; Bo Zheng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Although multi-task learning (MTL) has been a preferred approach and successfully applied in many real-world scenarios, MTL models are not guaranteed to outperform single-task models on all tasks mainly due to the negative effects of conflicting gradients among the tasks. In this paper, we fully examine the influence of conflicting gradients and further emphasize the importance and advantages of achieving non-conflicting gradients which allows simple but effective trade-off strategies among the tasks with stable performance. Base on our findings, we propose the Gradient Deconfliction via Orthogonal Projections onto Subspaces (GradOPS) spanned by other task-specific gradients. Our method not only solves all conflicts among the tasks, but can also effectively search for diverse solutions towards different trade-off preferences among the tasks. Theoretical analysis on convergence is provided, and performance of our algorithm is fully testified on multiple benchmarks in various domains. Results demonstrate that our method can effectively find multiple state-of-the-art solutions with different trade-off strategies among the tasks on multiple datasets.
TaylorNet: A Taylor-Driven Generic Neural Architecture
Authors: Hongjue Zhao; Yizhuo Chen; Dachun Sun; Yingdong Hu; Kaizhao Liang; Yanbing Mao; Lui Sha; Huajie Shao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Physics-informed machine learning (PIML) aims to incorporate physics knowledge into deep neural networks (DNNs) to improve the model generalization. However, existing methods in PIML are either designed for specific problems or hard to interpret the results using black-box DNNs. In this work, we propose Taylor Neural Network (TaylorNet), a generic neural architecture that parameterizes Taylor polynomials using DNNs without using non-linear activation functions. The key challenges of developing TaylorNet lie in: (i) mitigating the curse of dimensionality caused by higher-order terms, and (ii) improving the stability of model training. To overcome these challenges, we first adopt Tucker decomposition to decompose the higher-order derivatives in Taylor expansion parameterized by DNNs into low-rank tensors. Then we propose a novel reducible TaylorNet to further reduce the computational complexity by removing more redundant parameters in the hidden layers. In order to improve training accuracy and stability, we develop a new Taylor initialization method. Finally, the proposed models are evaluated on a broad spectrum of applications, including image classification, natural language processing (NLP), and dynamical systems. The results demonstrate that our proposed Taylor-Mixer, which replaces MLP and activation layers in the MLP-Mixer with Taylor layer, can achieve comparable accuracy on image classification, and similarly on sentiment analysis in NLP, while significantly reducing the number of model parameters. More importantly, our method can interpret some dynamical systems with Taylor polynomials. Meanwhile, the results demonstrate that our Taylor initialization can significantly improve classification accuracy compared to Xavier and Kaiming initialization.
SPRINT: Scalable Semantic Policy Pre-training via Language Instruction Relabeling
Authors: Jesse Zhang; Karl Pertsch; Jiahui Zhang; Taewook Nam; Sung Ju Hwang; Xiang Ren; Joseph J Lim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose SPRINT, an approach for scalable offline policy pre-training based on natural language instructions. SPRINT pre-trains an agent’s policy to execute a diverse set of semantically meaningful skills that it can leverage to learn new tasks faster. Prior work on offline pre-training required tedious manual definition of pre-training tasks or learned semantically meaningless skills via random goal-reaching. Instead, our approach SPRINT (Scalable Pre-training via Relabeling Language INsTructions) leverages natural language instruction labels on offline agent experience, collected at scale (e.g., via crowd-sourcing), to define a rich set of tasks with minimal human effort. Furthermore, by using natural language to define tasks, SPRINT can use pre-trained large language models to automatically expand the initial task set. By relabeling and aggregating task instructions, even across multiple training trajectories, we can learn a large set of new skills during pre-training. In experiments using a realistic household simulator, we show that agents pre-trained with SPRINT learn new long-horizon household tasks substantially faster than with previous pre-training approaches.