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Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
For the traveling salesman problem (TSP), the existing supervised learni...
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Learning Graphons via Structured Gromov-Wasserstein Barycenters
We propose a novel and principled method to learn a nonparametric graph ...
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A Hypergradient Approach to Robust Regression without Correspondence
We consider a regression problem, where the correspondence between input...
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Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation (OPE) est...
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Inductive Relational Matrix Completion
Data sparsity and cold-start issues emerge as two major bottlenecks for ...
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GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, b...
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Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and infere...
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Network Diffusions via Neural Mean-Field Dynamics
We propose a novel learning framework based on neural mean-field dynamic...
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Hessian-Free High-Resolution Nesterov Acceleration for Sampling
We propose an accelerated-gradient-based MCMC method. It relies on a mod...
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Hessian-Free High-Resolution Nesterov Accelerationfor Sampling
We propose an accelerated-gradient-based MCMC method. It relies on a mod...
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Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Traditional centralized multi-agent reinforcement learning (MARL) algori...
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Learning Cost Functions for Optimal Transport
Learning the cost function for optimal transport from observed transport...
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Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event seque...
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Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
Common Stochastic Gradient MCMC methods approximate gradients by stochas...
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Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Session-based target behavior prediction aims to predict the next item t...
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Differentiable Top-k Operator with Optimal Transport
The top-k operation, i.e., finding the k largest or smallest elements fr...
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Hyper-Meta Reinforcement Learning with Sparse Reward
Despite their success, existing meta reinforcement learning methods stil...
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Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism u...
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Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation
Generative Adversarial Networks (GANs) have achieved great success in un...
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Learning Stochastic Behaviour of Aggregate Data
Learning nonlinear dynamics of aggregate data is a challenging problem s...
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Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
Domain-adapted sentiment classification refers to training on a labeled ...
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Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
Human players in professional team sports achieve high level coordinatio...
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Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
Generalized zero-shot learning (GZSL) tackles the problem of learning to...
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Single Episode Policy Transfer in Reinforcement Learning
Transfer and adaptation to new unknown environmental dynamics is a key c...
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Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
We consider off-policy policy evaluation when the trajectory data are ge...
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Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn low-dimensional represent...
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Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models
Deep generative models are generally categorized into explicit models an...
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Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a world-wide problem that ca...
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Meta Learning with Relational Information for Short Sequences
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes...
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Modeling Event Propagation via Graph Biased Temporal Point Process
Temporal point process is widely used for sequential data modeling. In t...
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Visual Anomaly Detection in Event Sequence Data
Anomaly detection is a common analytical task that aims to identify rare...
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Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Temporal point process is an expressive tool for modeling event sequence...
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On Scalable and Efficient Computation of Large Scale Optimal Transport
Optimal Transport (OT) naturally arises in many machine learning applica...
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Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly mat...
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CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
We propose CM3, a new deep reinforcement learning method for cooperative...
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LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex...
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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
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Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Dynamic treatment recommendation systems based on large-scale electronic...
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Learning to Optimize via Wasserstein Deep Inverse Optimal Control
We study the inverse optimal control problem in social sciences: we aim ...
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Iterative Learning with Open-set Noisy Labels
Large-scale datasets possessing clean label annotations are crucial for ...
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Representation Learning over Dynamic Graphs
How can we effectively encode evolving information over dynamic graphs i...
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A Fast Proximal Point Method for Wasserstein Distance
Wasserstein distance plays increasingly important roles in machine learn...
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Learning to Recommend via Inverse Optimal Matching
We consider recommendation in the context of optimal matching, i.e., we ...
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Visually Explainable Recommendation
Images account for a significant part of user decisions in many applicat...
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Decoupled Learning for Factorial Marked Temporal Point Processes
This paper introduces the factorial marked temporal point process model ...
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tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a cr...
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Hawkes Processes for Invasive Species Modeling and Management
The spread of invasive species to new areas threatens the stability of e...
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Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large pop...
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Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations
We consider the problem of representing a large population's behavior po...
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