Search Results for author:
Found 17 papers, 10 papers with code
Dynamic Black-Litterman
By exploiting the underlying graphical structure relating the asset prices and views, we derive the conditional distribution of asset returns when the price process is geometric Brownian motion, and show that it can be written in terms of a multi-dimensional Brownian bridge.
Proteus: A Self-Designing Range Filter
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
PointBA: Towards Backdoor Attacks in 3D Point Cloud
We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks.
Digraph Inception Convolutional Networks
Graph Convolutional Networks (GCNs) have shown promising results in modeling graph-structured data.
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting
The EFM model is significantly different from the original Factorization Machines (FM) from two-fold: (1) the attribute-level formulation for explanatory variables and (2) exponential formulation for the positive response variable.
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint
Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
Directed Graph Convolutional Network
Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data.
Efficient Approximation Algorithms for Adaptive Influence Maximization
In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.
Social and Information Networks
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks
Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set.
Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances.
Learning Improvement Heuristics for Solving Routing Problems
In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems.
Learning Robust Features using Deep Learning for Automatic Seizure Detection
We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.
A Tabu Search Algorithm for the Multi-period Inspector Scheduling Problem
Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time.
An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem
The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved.
