OpenRaiser/Intern-Atlas huggingface dataset:
Hyperaid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering
Authors: Eli Chien, Puoya Tabaghi, Olgica Milenkovic
Venue: Conference KDD 2022
Year: 2022

The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applications in natural language processing, phylogeny, cancer genomics and a myriad of problem areas that involve hierarchical clustering. Despite the existence of several provably exact algorithms for tree-metric fitting of data that inherently obeys tree-metric constraints, much less is known about how to best fit tree-metrics for data whose structure moderately (or substantially) differs from a tree. For such noisy data, most available algorithms perform poorly and often produce negative edge weights in representative trees. Furthermore, it is currently not known how to choose the most suitable approximation objective for noisy fitting. Our contributions are as follows. First, we propose a new approach to tree-metric denoising (HyperAid) in hyperbolic spaces which transforms the original data into data that is ``more'' tree-like, when evaluated in terms of Gromov's $δ$ hyperbolicity. Second, we perform an ablation study involving two choices for the approximation objective, $\ell_p$ norms and the Dasgupta loss. Third, we integrate HyperAid with schemes for enforcing nonnegative edge-weights. As a result, the HyperAid platform outperforms all other existing methods in the literature, including Neighbor Joining (NJ), TreeRep and T-REX, both on synthetic and real-world data. Synthetic data is represented by edge-augmented trees and shortest-distance metrics while the real-world datasets include Zoo, Iris, Glass, Segmentation and SpamBase; on these datasets, the average improvement with respect to NJ is $125.94\%$.
Deep uncertainty quantification: A machine learning approach for weather forecasting
Authors: Bin Wang, Jie Lu, Zheng Yan, Huaishao Luo, Tianrui Li, Yu Zheng, Guangquan Zhang
Venue: Conference KDD 2019
Year: 2019

Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset. The preliminary version of the proposed method won 2nd place in an online competition for daily weather forecasting.
Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics
Authors: Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka
Venue: Conference ICLR 2021
Year: 2021

Understanding the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived from real-world datasets. We circumvent this obstacle through a unifying theoretical framework based on intrinsic symmetries embedded in a network's architecture that are present for any dataset. We show that any such symmetry imposes stringent geometric constraints on gradients and Hessians, leading to an associated conservation law in the continuous-time limit of stochastic gradient descent (SGD), akin to Noether's theorem in physics. We further show that finite learning rates used in practice can actually break these symmetry induced conservation laws. We apply tools from finite difference methods to derive modified gradient flow, a differential equation that better approximates the numerical trajectory taken by SGD at finite learning rates. We combine modified gradient flow with our framework of symmetries to derive exact integral expressions for the dynamics of certain parameter combinations. We empirically validate our analytic expressions for learning dynamics on VGG-16 trained on Tiny ImageNet. Overall, by exploiting symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset.
SCALOR: Generative World Models with Scalable Object Representations
Authors: Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, Sungjin Ahn
Venue: Conference ICLR 2020
Year: 2020

Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a probabilistic generative world model for learning SCALable Object-oriented Representation of a video. With the proposed spatially-parallel attention and proposal-rejection mechanisms, SCALOR can deal with orders of magnitude larger numbers of objects compared to the previous state-of-the-art models. Additionally, we introduce a background module that allows SCALOR to model complex dynamic backgrounds as well as many foreground objects in the scene. We demonstrate that SCALOR can deal with crowded scenes containing up to a hundred objects while jointly modeling complex dynamic backgrounds. Importantly, SCALOR is the first unsupervised object representation model shown to work for natural scenes containing several tens of moving objects.
Rlogic: Recursive logical rule learning from knowledge graphs
Authors: Kewei Cheng, Jiahao Liu, Wei Wang 0010, Yizhou Sun
Venue: Conference KDD 2022
Year: 2022

Logical rules are widely used to represent domain knowledge and hypothesis, which is fundamental to symbolic reasoning-based human intelligence. Very recently, it has been demonstrated that integrating logical rules into regular learning tasks can further enhance learning performance in a label-efficient manner. Many attempts have been made to learn logical rules automatically from knowledge graphs (KGs). However, a majority of existing methods entirely rely on observed rule instances to define the score function for rule evaluation and thus lack generalization ability and suffer from severe computational inefficiency. Instead of completely relying on rule instances for rule evaluation, RLogic defines a predicate representation learning-based scoring model, which is trained by sampled rule instances. In addition, RLogic incorporates one of the most significant properties of logical rules, the deductive nature, into rule learning, which is critical especially when a rule lacks supporting evidence. To push deductive reasoning deeper into rule learning, RLogic breaks a big sequential model into small atomic models in a recursive way. Extensive experiments have demonstrated that RLogic is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.
Statistical predicate invention
Authors: Stanley Kok, Pedro M. Domingos
Venue: Conference ICML 2007
Year: 2007

We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in structured data, and generalizes hidden variable discovery in statistical models and predicate invention in ILP. We propose an initial model for SPI based on second-order Markov logic, in which predicates as well as arguments can be variables, and the domain of discourse is not fully known in advance. Our approach iteratively refines clusters of symbols based on the clusters of symbols they appear in atoms with (e.g., it clusters relations by the clusters of the objects they relate). Since different clusterings are better for predicting different subsets of the atoms, we allow multiple cross-cutting clusterings. We show that this approach outperforms Markov logic structure learning and the recently introduced infinite relational model on a number of relational datasets.
R5: Rule discovery with reinforced and recurrent relational reasoning
Authors: Shengyao Lu, Bang Liu 0003, Keith G. Mills, Shangling Jui, Di Niu
Venue: Conference ICLR 2022
Year: 2022

Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules.
BSODA: a bipartite scalable framework for online disease diagnosis
Authors: Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen
Venue: Conference WWW 2022
Year: 2022

A growing number of people are seeking healthcare advice online. Usually, they diagnose their medical conditions based on the symptoms they are experiencing, which is also known as self-diagnosis. From the machine learning perspective, online disease diagnosis is a sequential feature (symptom) selection and classification problem. Reinforcement learning (RL) methods are the standard approaches to this type of tasks. Generally, they perform well when the feature space is small, but frequently become inefficient in tasks with a large number of features, such as the self-diagnosis. To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA. BSODA is composed of two cooperative branches that handle symptom-inquiry and disease-diagnosis, respectively. The inquiry branch determines which symptom to collect next by an information-theoretic reward. We employ a Product-of-Experts encoder to significantly improve the handling of partial observations of a large number of features. Besides, we propose several approximation methods to substantially reduce the computational cost of the reward to a level that is acceptable for online services. Additionally, we leverage the diagnosis model to estimate the reward more precisely. For the diagnosis branch, we use a knowledge-guided self-attention model to perform predictions. In particular, BSODA determines when to stop inquiry and output predictions using both the inquiry and diagnosis models. We demonstrate that BSODA outperforms the state-of-the-art methods on several public datasets. Moreover, we propose a novel evaluation method to test the transferability of symptom checking methods from synthetic to real-world tasks. Compared to existing RL baselines, BSODA is more effectively scalable to large search spaces.
Acekg: A large-scale knowledge graph for academic data mining
Authors: Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Weinan Zhang, Xinbing Wang
Venue: Conference CIKM 2018
Year: 2018

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG.
Benchmarking the spectrum of agent capabilities
Authors: Danijar Hafner
Venue: Conference ICLR 2022
Year: 2022

Evaluating the general abilities of intelligent agents requires complex simulation environments. Existing benchmarks typically evaluate only one narrow task per environment, requiring researchers to perform expensive training runs on many different environments. We introduce Crafter, an open world survival game with visual inputs that evaluates a wide range of general abilities within a single environment. Agents either learn from the provided reward signal or through intrinsic objectives and are evaluated by semantically meaningful achievements that can be unlocked during each episode, such as discovering resources and crafting tools. Consistently unlocking all achievements requires strong generalization, deep exploration, and long-term reasoning. We experimentally verify that Crafter is of appropriate difficulty to drive future research and provide baselines scores of reward agents and unsupervised agents. Furthermore, we observe sophisticated behaviors emerging from maximizing the reward signal, such as building tunnel systems, bridges, houses, and plantations. We hope that Crafter will accelerate research progress by quickly evaluating a wide spectrum of abilities.
Dynamic Preferences in Multi-criteria Reinforcement Learning
Authors: Sriraam Natarajan, Prasad Tadepalli
Venue: Conference ICML 2005
Year: 2005

The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made among multiple objectives. Moreover, the agent's preferences between different objectives may vary with time. In this paper, we consider the problem of learning in the presence of time-varying preferences among multiple objectives, using numeric weights to represent their importance. We propose a method that allows us to store a finite number of policies, choose an appropriate policy for any weight vector and improve upon it. The idea is that although there are infinitely many weight vectors, they may be well-covered by a small number of optimal policies. We show this empirically in two domains: a version of the Buridan's ass problem and network routing.
Disentangled dynamic heterogeneous graph learning for opioid overdose prediction
Authors: Qianlong Wen, Zhongyu Ouyang, Jianfei Zhang 0002, Yiyue Qian, Yanfang Ye 0001, Chuxu Zhang
Venue: Conference KDD 2022
Year: 2022

Opioids (e.g., oxycodone and morphine) are highly addictive prescription (aka Rx) drugs which can be easily overprescribed and lead to opioid overdose. Recently, the opioid epidemic is increasingly serious across the US as its related deaths have risen at alarming rates. To combat the deadly opioid epidemic, a state-run prescription drug monitoring program (PDMP) has been established to alleviate the drug over-prescribing problem in the US. Although PDMP provides a detailed prescription history related to opioids, it is still not enough to prevent opioid overdose because it cannot predict over-prescribing risk. In addition, existing machine learning-based methods mainly focus on drug doses while ignoring other prescribing patterns behind patients' historical records, thus resulting in suboptimal performance. To this end, we propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, we abstract the PDMP data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, we design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, we devise an adversarial disentangler to learn a disentangled representation which is particularly related to the prescribing patterns. Extensive experiments on a 1-year anonymous PDMP data demonstrate that DDHGNN outperforms state-of-the-art methods, revealing its promising future in preventing opioid overdose.
Cascade variational auto-encoder for hierarchical disentanglement
Authors: Fudong Lin, Xu Yuan, Lu Peng, Nian-Feng Tzeng
Venue: Conference CIKM 2022
Year: 2022

While deep generative models pave the way for many emerging applications, decreased interpretability for larger model sizes and complexities hinders their generalizability to wide domains such as economy, security, healthcare, etc. Considering this obstacle, a common practice is to learn interpretable representations through latent feature disentanglement, aiming for exposing a set of mutually independent factors of data variations. However, existing methods either fail to catch the trade-off between the synthetic data quality and model interpretability, or consider the first-order feature disentangling only, overlooking the fact that a subset of salient features can carry decomposable semantic meanings and hence be of high-order in nature. Hence, we in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularize on a cascade Variational Auto-Encoder (VAE). Specifically, this regularizer guides the learner to discover a representation space that comprises both first-order disentangled features and high-order salient features, with the feature interplay captured by the Bayesian structure. Experiments demonstrate that this regularizer gives us free control over the representation space and can guide the learner to discover decomposable semantic meanings by capturing the interplay among independent factors. Meanwhile, we benchmark extensive experiments on six widely-used vision datasets, and the results exhibit that our approach outperforms the state-of-the-art VAE competitors in terms of the trade-off between the synthetic data quality and model interpretability. Although our design is framed in the VAE regime, it in effect is generic and can be better amenable to both GANs and VAEs in terms of letting them concurrently enjoy both high model interpretability and high synthesis quality.
Xgnn: Towards model-level explanations of graph neural networks
Authors: Hao Yuan 0001, Jiliang Tang, Xia Hu 0001, Shuiwang Ji
Venue: Conference KDD 2020
Year: 2020

Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information, which have achieved promising performance on many graph tasks. However, GNNs are mostly treated as black-boxes and lack human intelligible explanations. Thus, they cannot be fully trusted and used in certain application domains if GNN models cannot be explained. In this work, we propose a novel approach, known as XGNN, to interpret GNNs at the model-level. Our approach can provide high-level insights and generic understanding of how GNNs work. In particular, we propose to explain GNNs by training a graph generator so that the generated graph patterns maximize a certain prediction of the model. We formulate the graph generation as a reinforcement learning task, where for each step, the graph generator predicts how to add an edge into the current graph. The graph generator is trained via a policy gradient method based on information from the trained GNNs. In addition, we incorporate several graph rules to encourage the generated graphs to be valid. Experimental results on both synthetic and real-world datasets show that our proposed methods help understand and verify the trained GNNs. Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
Self-stabilization: The implicit bias of gradient descent at the edge of stability
Authors: Alex Damian, Eshaan Nichani, Jason D. Lee
Venue: Conference ICLR 2023
Year: 2023

Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness $S(θ)$, is bounded by $2/η$, training is "stable" and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural networks with full batch or large batch gradient descent. Most recently, Cohen et al. (2021) observed two important phenomena. The first, dubbed progressive sharpening, is that the sharpness steadily increases throughout training until it reaches the instability cutoff $2/η$. The second, dubbed edge of stability, is that the sharpness hovers at $2/η$ for the remainder of training while the loss continues decreasing, albeit non-monotonically. We demonstrate that, far from being chaotic, the dynamics of gradient descent at the edge of stability can be captured by a cubic Taylor expansion: as the iterates diverge in direction of the top eigenvector of the Hessian due to instability, the cubic term in the local Taylor expansion of the loss function causes the curvature to decrease until stability is restored. This property, which we call self-stabilization, is a general property of gradient descent and explains its behavior at the edge of stability. A key consequence of self-stabilization is that gradient descent at the edge of stability implicitly follows projected gradient descent (PGD) under the constraint $S(θ) \le 2/η$. Our analysis provides precise predictions for the loss, sharpness, and deviation from the PGD trajectory throughout training, which we verify both empirically in a number of standard settings and theoretically under mild conditions. Our analysis uncovers the mechanism for gradient descent's implicit bias towards stability.