GenAI4ELab/papercli-papers huggingface dataset:
ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Authors: Han Lu; Zenan Li; Runzhong Wang; Qibing Ren; Xijun Li; Mingxuan Yuan; Jia Zeng; Xiaokang Yang; Junchi Yan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Solving combinatorial optimization (CO) on graphs has been attracting increasing interests from the machine learning community whereby data-driven approaches were recently devised to go beyond traditional manually-designated algorithms. In this paper, we study the robustness of a combinatorial solver as a blackbox regardless it is classic or learning-based though the latter can often be more interesting to the ML community. Specifically, we develop a practically feasible robustness metric for general CO solvers. A no-worse optimal cost guarantee is developed as such the optimal solutions are not required to achieve for solvers, and we tackle the non-differentiable challenge in input instance disturbance by resorting to black-box adversarial attack methods. Extensive experiments are conducted on 14 unique combinations of solvers and CO problems, and we demonstrate that the performance of state-of-the-art solvers like Gurobi can degenerate by over 20% under the given time limit bound on the hard instances discovered by our robustness metric, raising concerns about the robustness of combinatorial optimization solvers.
Safer Reinforcement Learning with Counterexample-guided Offline Training
Authors: Xiaotong Ji; Antonio Filieri
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Safe reinforcement learning (RL) aims at addressing the limitation of reinforcement learning in safety-critical scenarios, where failures during learning may incur high costs. Several methods exist to incorporate external knowledge or to use proximal sensor data to limit the exploration of unsafe states. However, dealing with (partially) unknown environments and dynamics, where an agent must discover safety threats during exploration, remains challenging. In this paper, we propose a method to abstract hybrid continuous-discrete systems into compact surrogate models representing the safety-relevant knowledge acquired by the agent at any time during exploration. We exploit probabilistic counterexamples generation to synthesise minimal, partial simulation environments from the surrogate model where the agent can train offline to produce heuristic strategies to minimise the risk of visiting unsafe states during subsequent online exploration. We demonstrate our method's effectiveness in increasing the agent's exploration safety on a selection of OpenAI Gym benchmarks.
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
Authors: Yufei Wang; Jiayi Zheng; Can Xu; Xiubo Geng; Tao Shen; Chongyang Tao; Daxin Jiang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper focuses on data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA), a Seq2Seq language model pretrained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model
(i.e., all-in-one). The resulting KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first to attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves the performance of the strong pre-trained language
models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL’03 and WikiAnn; ii) KnowDA successful transfer the task knowledge to NLP tasks whose types are seen and unseen in KoMT.
Versatile Neural Processes for Learning Implicit Neural Representations
Authors: Zongyu Guo; Cuiling Lan; Zhizheng Zhang; Yan Lu; Zhibo Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical solution for fast inference of continuous functions. However, existing NP architectures suffer from inferior modeling capability for complex signals. In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability of approximating functions. Specifically, we introduce a bottleneck encoder that produces fewer and informative context tokens, relieving the high computational cost while providing high modeling capability. At the decoder side, we hierarchically learn multiple global latent variables that jointly model the global structure and the uncertainty of a function, enabling our model to capture the distribution of complex signals. We demonstrate the effectiveness of the proposed VNP on a variety of tasks involving 1D, 2D and 3D signals. Particularly, our method shows promise in learning accurate INRs w.r.t. a 3D scene without further finetuning.
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes
Authors: Christian Alexander Haase; Christoph Hertrich; Georg Loho
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width. More precisely, we show that $\lceil\log_2(n)\rceil$ hidden layers are indeed necessary to compute the maximum of $n$ numbers, matching known upper bounds. Our results are based on the known duality between neural networks and Newton polytopes via tropical geometry. The integrality assumption implies that these Newton polytopes are lattice polytopes. Then, our depth lower bounds follow from a parity argument on the normalized volume of faces of such polytopes.
Optimising 2D Pose Representation: Improving Accuracy, Stability and Generalisability inUnsupervised 2D-3D Human Pose Estimation
Authors: Peter Timothy David Hardy; Srinandan Dasmahapatra; Hansung Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper addresses the problem of 2D pose representation during unsupervised 2D to 3D pose lifting to improve the accuracy, stability and generalisability of 3D human pose estimation (HPE) models. All unsupervised 2D-3D HPE approaches provide the entire 2D kinematic skeleton to a model during training. We argue that this is sub-optimal and disruptive as long-range correlations are induced between independent 2D key points and predicted 3D ordinates during training. To this end, we conduct the following study. With a maximum architecture capacity of 6 residual blocks, we evaluate the performance of 5 models which each represent a 2D pose differently during the adversarial unsupervised 2D-3D HPE process. Additionally, we show the correlations between 2D key points which are learned during the training process, highlighting the unintuitive correlations induced when an entire 2D pose is provided to a lifting model. Our results show that the most optimal representation of a 2D pose is that of two independent segments, the torso and legs, with no shared features between each lifting network. This approach decreased the average error by 20% on the Human3.6M dataset when compared to a model with a near identical parameter count trained on the entire 2D kinematic skeleton. Furthermore, due to the complex nature of adversarial learning, we show how this representation can also improve convergence during training allowing for an optimum result to be obtained more often.
A Hierarchical Bayesian Approach to Federated Learning
Authors: Minyoung Kim; Timothy Hospedales
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a novel hierarchical Bayesian approach to Federated learning (FL), where our models reasonably describe the generative process of clients' local data via hierarchical Bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate. Interestingly, the variational inference in our Bayesian model leads to an optimization problem whose block-coordinate descent  solution becomes a distributed algorithm that is separable over clients and allows them not to reveal their own private data at all, thus fully compatible with FL. We also highlight that our block-coordinate algorithm has particular forms that subsume the well-known FL algorithms including Fed-Avg and Fed-Prox as special cases. That is, we not only justify the previous Fed-Avg and Fed-Prox algorithms whose learning protocols look intuitive but theoretically less underpinned, but also generalise them even further via principled Bayesian approaches. Beyond introducing novel modeling and derivations, we also offer convergence analysis showing that our block-coordinate FL algorithm converges to an (local) optimum of the objective at the rate of $O(1/\sqrt{t})$, the same rate as regular (centralised) SGD, as well as the generalisation error analysis where we prove that the test error of our model on unseen data is guaranteed to vanish as we increase the training data size, thus asymptotically optimal.
Conceptual SCAN: Learning With and About Rules
Authors: Nathan Scales; Nathanael Schärli; Abubakr Babiker; Yu-Han Liu; Mostafa Dehghani; Olivier Bousquet
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The ability to learn from a mix of rules and examples and to reflect on the learned abstractions is an important aspect of human intelligence. At the same time, there is a lack of benchmarks that systematically test for this ability, which makes it hard to evaluate the degree to which it is present in state-of-the-art ML architectures. We introduce a method to systematically construct such benchmarks by using an example structure that allows us to explicitly provide and ask about rules that are relevant for the given task. We present a simple dataset that is constructed according to this method, and we use it to analyze the performance of a variety of T5-based machine learning models. We identify four challenge areas in this setup: maintaining consistency between learned rules and their application, scaling to larger rule sets, compositional generalization, and dealing with limited training data.
System Identification as a Reinforcement Learning Problem
Authors: Jose Antonio Martin H.; Oscar Fernández Vicente; Sergio Perez; Anas Belfadil; Cristina Ibanez-Llano; Freddy Perozo; Jose Javier Valle; Javier Arechalde Pelaz
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering in different fields. Particularly, it is a recurring theme in Reinforcement Learning research, where forward models approximate the state transition function of a Markov Decision Process by learning a mapping function from current state and action to the next state. This problem is commonly defined as a Supervised Learning problem in a direct way. This common approach faces several difficulties due to the inherent complexities of the dynamics to learn, for example, delayed effects, high non-linearity, non-stationarity, partial observability and, more important, error accumulation when using bootstrapped predictions (predictions based on past predictions), over large time horizons. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.
LogicDP: Creating Labels for Graph Data via Inductive Logic Programming
Authors: Yuan Yang; Faramarz Fekri; James Clayton Kerce; Ali Payani
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Graph data, such as scene graphs and knowledge graphs, see wide use in AI systems. In real-world and large applications graph data are usually incomplete, motivating graph reasoning models for missing-fact or missing-relationship inference. While these models can achieve state-of-the-art performance, they require a large amount of training data.

Recent years have witnessed the rising interest in label creation with data programming (DP) methods, which aim to generate training labels from heuristic labeling functions. However, existing methods typically focus on unstructured data and are not optimized for graphs. In this work, we propose LogicDP, a data programming framework for graph data. Unlike existing DP methods, (1) LogicDP utilizes the inductive logic programming (ILP) technique and automatically discovers the labeling functions from the graph data; (2) LogicDP employs a budget-aware framework to iteratively refine the functions by querying an oracle, which significantly reduces the human efforts in function creations. Experiments show that LogicDP achieves better data efficiency in both scene graph and knowledge graph reasoning tasks.
ReaKE: Contrastive Molecular Representation Learning with Chemical Synthetic Knowledge Graph
Authors: Yi Wang; Shuangjia Zheng; Jiahua Rao; Yunan Luo; Yuedong Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Molecular representation learning has demonstrated great promise in bridging machine learning and chemical science and in supporting novel chemical discoveries. State-of-the-art methods mostly employ graph neural networks (GNNs) with self-supervised learning (SSL) and extra chemical reaction knowledge to empower the learned embeddings. However, prior works ignore three major issues in modeling reaction data, that is abnormal energy flow, ambiguous embeddings, and sparse embedding space problems. To address these problems, we propose ReaKE, a chemical synthetic knowledge graph-driven pre-training framework for molecular representation learning. We first construct a large-scale chemical synthetic knowledge graph comprising reactants, products and reaction rules. We then propose triplet-level and graph-level contrastive learning strategies to jointly optimize the knowledge graph and molecular embeddings. Representations learned by ReaKE can capture intermolecular relationships reflected in the semantic knowledge graph and molecular structures. By comparing with other state-of-the-art methods, we show that ReaKE can achieve competitive performance on the reaction prediction pretext task and the learned representations transfer well to various downstream tasks, including reaction classification, yield prediction, and molecule property prediction. Further visualization shows that the learned representations can capture the fine-grained differences both between reactions and between molecules.
Revisiting Domain Randomization Via Relaxed State-Adversarial Policy Optimization
Authors: Yun-Hsuan Lien; Ping-Chun Hsieh; Yu-Shuen Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Domain randomization (DR) is widely used in reinforcement learning (RL) to bridge the gap between simulation and reality through maximizing its average returns under the perturbation of environmental parameters. Although effective, the methods have two limitations: (1) Even the most complex simulators cannot capture all details in reality due to finite domain parameters and simplified physical models. (2) Previous methods often assume that the distribution of domain parameters is a specific family of probability functions, such as a normal or a uniform distribution, which may not be correct. To enable robust RL via DR without the aforementioned limitations, we rethink DR from the perspective of adversarial state perturbation, without the need for re-configuring the simulator or relying on prior knowledge about the environment. We point out that perturbing agents to the worst states during training is naive and could make the agents over-conservative. Hence, we present a Relaxed State-Adversarial Algorithm to tackle the over-conservatism issue by simultaneously maximizing the average-case and worst-case performance of policies. We compared our method to the state-of-the-art methods for evaluation. Experimental results and theoretical proofs verified the effectiveness of our method.
Explanation Uncertainty with Decision Boundary Awareness
Authors: Davin Hill; Aria Masoomi; Sandesh Ghimire; Max Torop; Jennifer Dy
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Post-hoc explanation methods have become increasingly depended upon for understanding black-box classifiers in high-stakes applications, precipitating a need for reliable explanations. While numerous explanation methods have been proposed, recent works have shown that many existing methods can be inconsistent or unstable. In addition, high-performing classifiers are often highly nonlinear and can exhibit complex behavior around the decision boundary, leading to brittle or misleading local explanations. Therefore, there is an impending need to quantify the uncertainty of such explanation methods in order to understand when explanations are trustworthy. We introduce a novel uncertainty quantification method parameterized by a Gaussian Process model, which combines the uncertainty approximation of existing methods with a novel geodesic-based similarity which captures the complexity of the target black-box decision boundary. The proposed framework is highly flexible—it can be used with any black-box classifier and feature attribution method to amortize uncertainty estimates for explanations. We show theoretically that our proposed geodesic-based kernel similarity increases with the complexity of the decision boundary. Empirical results on multiple tabular and image datasets show that our decision boundary-aware uncertainty estimate improves understanding of explanations as compared to existing methods
Asynchronous Message Passing: A new Framework for Learning in Graphs
Authors: Lukas Faber; Roger Wattenhofer
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This paper studies asynchronous message passing (AMP), a new framework for applying neural networks to graphs. Existing graph neural networks (GNNs) use the message passing framework which is based on the synchronous distributed computing model. In traditional GNNs, nodes aggregate their neighbors in each round, which causes problems such as oversmoothing and expressiveness limitations. On the other hand, our AMP framework is based on the \textit{asynchronous} model, where nodes react to messages of their neighbors individually. We prove (i)  AMP is at least as powerful as the message passing framework, (ii) AMP is more powerful than the $1-$WL test for graph isomorphism, an important benchmark for message passing GNNs, and (iii) conceptually, AMP can even separate any pair of graphs and compute graph isomorphism. We experimentally validate the findings on AMP's expressiveness, and show that AMP might be better suited to propagate messages over large distances in graphs. We also demonstrate that AMP performs well on several graph classification benchmarks.
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Authors: Hua Wang; Sheng Gao; Huanyu Zhang; Milan Shen; Weijie J Su
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition. In this study, we introduce the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees of private algorithms. The Edgeworth Accountant starts by losslessly tracking the privacy loss under composition using the $f$-differential privacy framework, which allows us to express the privacy guarantees using privacy-loss log-likelihood ratios (PLLRs). As the name suggests, this accountant next uses the Edgeworth expansion to the upper and lower bounds the probability distribution of the sum of the PLLRs. Moreover, by relying on a technique for approximating complex distributions using simple ones, we demonstrate that the Edgeworth Accountant can be applied to the composition of any noise-addition mechanism. Owing to certain appealing features of the Edgeworth expansion, the $(\epsilon, \delta)$-differential privacy bounds offered by this accountant are non-asymptotic, with essentially no extra computational cost, as opposed to the prior approaches, wherein the running times increase with the number of compositions. Finally, we demonstrate that our upper and lower $(\epsilon, \delta)$-differential privacy bounds are tight in federated analytics and certain regimes of training private deep learning models.
A Differentiable Loss Function for Learning Heuristics in A*
Authors: Leah Chrestien; Tomáš Pevný; Antonin Komenda; Stefan Edelkamp
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values. This paper argues that this does not necessarily lead to a faster search of A* algorithm since its execution relies on relative values instead of absolute ones. As a mitigation, we propose a L* loss, which upper-bounds the number of excessively expanded states inside the A* search. The L* loss, when used in the optimization of state-of-the-art deep neural networks for automated planning in maze domains like Sokoban and maze with teleports, significantly improves the fraction of solved problems, the quality of founded plans, and reduces the number of expanded states to approximately 50%
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Authors: Bowen Jin; Yu Zhang; Yu Meng; Jiawei Han
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network representation learning models focus on propagating and aggregating node attributes, lacking specific designs to utilize text semantics on edges. While there exist edge-aware graph neural networks, they directly initialize edge attributes as a feature vector, which cannot fully capture the contextualized text semantics of edges. In this paper, we propose Edgeformers, a framework built upon graph-enhanced Transformers, to perform edge and node representation learning by modeling texts on edges in a contextualized way. Specifically, in edge representation learning, we inject network information into each Transformer layer when encoding edge texts; in node representation learning, we aggregate edge representations through an attention mechanism within each node’s ego-graph. On five public datasets from three different domains, Edgeformers consistently outperform state-of-the-art baselines in edge classification and link prediction, demonstrating the efficacy in learning edge and node representations, respectively.