Combines global and local features for better social circle prediction in ego-networks.
problem Efficiently analyzing ego-networks with hidden local structures.
method Evolved deep learning techniques to capture both global and local network features.
result Social circle prediction benefits from a combination of global and local features.
Ego-CNN detects critical structures in graphs efficiently.
problem Lack of precise detection of critical structures in existing graph embedding models.
method Ego-CNN uses ego-convolutions at each layer and stacks them in an ego-centric way.
result Ego-CNN achieves comparable task performance to state-of-the-art models and can incorporate scale-free priors.
This work improves GNN training efficiency by maximizing ego-graph information.
problem Training dedicated GNNs is costly for large-scale graphs.
method Proposes EGI (Ego-Graph Information maximization) to capture essential graph information and establish a theoretical framework for transfer learning.
result Demonstrates the effectiveness of EGI in improving GNN training efficiency and transferability.
Paper establishes no-regret property for practical EGO optimization.
problem No theoretical bounds on cumulative regret for practical EGO.
method Introduced practical EGO with a positive nugget, analyzed its regret bounds.
result Practical EGO is a no-regret algorithm with sublinear regret bounds.
TREGO improves EGO for global optimization of high-dimensional problems.
problem Efficient Global Optimization struggles with high dimensions and lacks theoretical guarantees.
method TREGO alternates between EGO steps and local steps within a trust region.
result TREGO outperforms EGO and other methods in black-box optimization problems.
Designing the architecture for an artificial neural network is a cumbersome task because of the numerous parameters to configure, including activation functions, layer types, and hyper-parameters. With the large number of parameters for most networks nowadays, it is intractable to find a good configuration for a given …
Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsuperv…
In this paper, we investigate the capability of the universal Kriging (UK) model for single-objective global optimization applied within an efficient global optimization (EGO) framework. We implemented this combined UK-EGO framework and studied four variants of the UK methods, that is, a UK with a first-order polynomia…
Study compares investor networks and cellphone communication networks to analyze layer structures.
problem Comparing investor networks and cellphone communication networks to understand information diffusion.
method Used clustering algorithms to detect layer structures in ego networks of both networks.
result Nodes in both networks can be divided into two groups with specific layer structures.
Heterophily affects GNN robustness; separating ego- and neighbor-embeddings improves defense.
problem The robustness of GNNs to adversarial attacks.
method Formalized relation between heterophily and GNN robustness; empirical analysis; design principles for improved robustness.
result Separating ego- and neighbor-embeddings increases GNN robustness.
This paper develops a coreset method for GNNs that speeds up training on large graphs.
problem Training Graph Neural Networks (GNNs) on large-scale graphs is computationally expensive.
method The paper proposes a spectral greedy coreset (SGGC) method that selects ego-graphs based on spectral embeddings.
result SGGC significantly speeds up GNN training on large graphs and outperforms other coreset methods.
EGO-MDA identifies optimal spectral-bands for process discrimination.
problem Optimal spectral-bands for process discrimination.
method EGO-MDA, an unsupervised method using EGO and Mixture Discriminant Analysis.
result EGO-MDA achieves at least 70% improvement in median deviance.
The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP. The important factor that controls the e…
Optimization of very expensive black-box functions requires utilization of maximum information gathered by the process of optimization. Model Guided Sampling Optimization (MGSO) forms a more robust alternative to Jones' Gaussian-process-based EGO algorithm. Instead of EGO's maximizing expected improvement, the MGSO use…
Study shows peers' graduation improves residents' success in TCs.
problem Identifying peer influence in therapeutic communities adjusting for latent homophily.
method Used data on affirmations and exit dates to form peer networks, modeled latent homophily, and proposed bias correction methods.
result Positive effect of peers' graduation on residents' graduation, varying by gender, race, and role model definition.
A Ph.D. thesis proposes an efficient approach for optimizing aircraft eco-design with high-dimensional mixed integer variables.
problem Optimizing aircraft eco-design with high-dimensional mixed integer variables.
method EGO optimization approach based on sequential enrichment of adaptive surrogate models, adapted for high-dimensional and mixed variable spaces.
result Effective parameterization tools significantly reduce the number of design variables, enabling optimization of complex aircraft concepts.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
Study examines AI's role in robo-investing, focusing on benefits for specific investors.
problem Understanding the benefits of robo-investing for different investor types.
method Used a unique data set of brokerage accounts, analyzed various robo-investing strategies, compared human vs. machine performance.
result AI can provide benefits to low-income and high-risk-averse investors.
New metrics prevent event collapse in contrast maximization frameworks.
problem Event collapse in contrast maximization frameworks.
method First principles of space-time deformation based on differential geometry and physics.
result Proposed metrics mitigate event collapse and do not harm well-posed warps.
Validates conformal prediction for network data under non-uniform sampling.
problem Validity of conformal prediction for network data under non-representative sampling.
method Interprets sampling mechanisms as selection rules, studies validity conditional on selection events, uses permutation invariance and joint exchangeability.
result Finite-sample validity of conformal prediction for certain selection events and asymptotic validity for random walk sampling.
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social ne…
Mixed-SCORE+ improves community detection in weak signal networks.
problem Detecting communities in weak signal networks.
method Proposes Mixed-SCORE+ combining properties of Mixed-SCORE and SCORE+.
result Significantly improves detection error rates on Polblogs and weak signal networks.
Graph neural networks struggle with heterophily, but new designs improve their performance.
problem Graph neural networks struggle with heterophily (networks where connected nodes may have different class labels and dissimilar features).
method Ego- and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations.
result The identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively.
This review covers methods for autonomous driving including tracking, prediction, and decision making.
problem Improving autonomous driving through better tracking, prediction, and decision making.
method Approaches based on neural networks, stochastic techniques, and reinforcement learning are discussed.
result Effective methods for autonomous driving are identified and compared.
Proposes a novel tensor-based approach for multi-level link prediction.
problem Inferring potential links from observed networks.
method Tensor-based joint network embedding capturing pairwise and hyperlinks.
result Improves hyperlink and pairwise link prediction accuracy.
End-to-end CNN for real-time MOD improves KITTI dataset accuracy by 8%.
problem Real-time detection of moving objects for autonomous vehicles.
method Spatio-temporal context and time-aware architecture using optical flow.
result Improvement of 8% in KITTI dataset accuracy compared to baselines.
This research tackles intervention-centric causal reasoning in learning agents by using meta-learning.
problem Learning agents lack the concept of interventions, making causal learning challenging.
method A meta-reinforcement learning algorithm is used to learn causal relationships from observational data.
result The approach enables agents to learn and manipulate the environment effectively.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
problem Accurate pedestrian intention prediction for autonomous vehicles in real-world scenarios.
method Recurrent and temporal attention-based model using kinematic and spatial features.
result PIP-Net predicts pedestrian crossing intentions up to 4 seconds in advance.
MIDAS learns to adaptively control other cars in urban driving scenarios.
problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.
The paper proposes a method to create robust neural networks for automated driving.
problem Creating neural networks that can accurately predict road conditions and distances.
method The method introduces a non-standard loss function with tolerance to account for label variability and allows for deviations from labels.
result The proposed method results in a neural network that can robustly predict road conditions and distances, even with small label variations.
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecolo…
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically …
Two approaches use TDA and graph theory for tennis match prediction.
problem Predicting tennis match outcomes using network features.
method Lower-star filtration on player competitive networks, Random Forest model, modified Katz similarity index.
result TDA features alone can achieve above-chance prediction in tennis match outcomes.
A new method for efficient structural node embeddings using Von Neumann entropy.
problem Efficiently identifying structurally equivalent nodes in complex networks.
method VNEstruct: a simple approach generating low-dimensional structural node embeddings using Von Neumann entropy.
result VNEstruct achieves robustness on structural role identification and state-of-the-art performance on graph classification tasks.
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly ext…
Improves reinforcement learning extrapolation in Gridworlds.
problem Generalizing to unseen states in reinforcement learning.
method Avoiding deterministic action choice, using ego-centric representation, incorporating symmetry, and adding an entropy term.
result Significant improvement in extrapolation performance in Gridworld environments.
In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discu…
Model predicts multi-agent trajectories using a differentiable simulator.
problem Predicting future positions of multiple interacting agents.
method Conditional recurrent variational neural networks (CVRNNs) with a kinematic bicycle model.
result Achieves state-of-the-art results on INTERACTION dataset.
MA-COPP predicts multi-agent system outcomes using data from a different policy, with probabilistic guarantees.
problem Predicting outcomes in multi-agent systems using data from a different policy.
method Conformal prediction framework applied to multi-agent systems, avoiding exhaustive search.
result Achieves probabilistic guarantees for multi-agent system predictions.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data …
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
problem Lack of efficient and adaptable evaluation methods for autonomous vehicles.
method Adaptive evaluation framework using ensemble models and nonparametric Bayesian clustering.
result Adversarial scenarios significantly degrade tested autonomous vehicles' performance.
Proposes a non-autoregressive Transformer for time series forecasting.
problem Autoregressive errors and spatial-temporal dependencies in time series forecasting.
method Introduces a Non-Autoregressive Transformer with a learned temporal influence map.
result Demonstrates state-of-the-art performance on time series forecasting datasets.
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, formulating a rule based decision maker for selecting maneuvers may not be ideal. Similarly, it may not be effective to design an a-priori cost…
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
problem Efficiently discovering rare failure events in autonomous vehicle simulations.
method Approximate dynamic programming and scene decomposition to estimate failure distribution.
result Increased number of failures discovered compared to baseline approaches.
This research tackles information design in multi-agent reinforcement learning.
problem Designing information to influence other adaptive agents in a non-stationary environment.
method Formulated Markov signaling game, introduced signaling gradient and extended obedience constraints.
result Developed efficient algorithm for mixed-motive tasks in multi-agent reinforcement learning.
Self-supervised method estimates distances on fisheye cameras for autonomous driving.
problem Accurate Euclidean distance estimation on fisheye cameras for autonomous driving.
method Self-supervised scale-aware framework for monocular fisheye videos.
result State-of-the-art results on KITTI dataset, comparable to other methods.
End-to-end multi-object tracking learns object interactions.
problem Object tracking ignores interactions between objects.
method End-to-end relational reasoning model MOHART.
result Relational reasoning improves tracking and prediction.