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06/29/2023
A Neural Separation Algorithm for the Rounded Capacity Inequalities
The cutting plane method is a key technique for successful branch-and-cu...
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06/09/2023
Computing Algorithm for an Equilibrium of the Generalized Stackelberg Game
The 1-N generalized Stackelberg game (single-leader multi-follower game)...
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06/05/2023
Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context
Min-max routing problems aim to minimize the maximum tour length among a...
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06/05/2023
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
This paper proposes Meta-SAGE, a novel approach for improving the scalab...
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06/05/2023
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
We study the problem of optimizing biological sequences, e.g., proteins,...
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06/02/2023
Symmetric Exploration in Combinatorial Optimization is Free!
Recently, deep reinforcement learning (DRL) has shown promise in solving...
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10/22/2022
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
Convolutional deep sets are the architecture of a deep neural network (D...
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05/26/2022
Collaborative Distillation Meta Learning for Simulation Intensive Hardware Design
This paper proposes a novel collaborative distillation meta learning (CD...
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03/13/2022
Neural Solvers for Fast and Accurate Numerical Optimal Control
Synthesizing optimal controllers for dynamical systems often involves so...
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10/26/2021
Learning Collaborative Policies to Solve NP-hard Routing Problems
Recently, deep reinforcement learning (DRL) frameworks have shown potent...
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06/22/2021
Continuous-Depth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuous-depth graph neural networks (GN...
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06/08/2021
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appr...
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06/07/2021
Differentiable Multiple Shooting Layers
We detail a novel class of implicit neural models. Leveraging time-paral...
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06/07/2021
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optim...
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06/06/2021
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
We propose ScheduleNet, a RL-based real-time scheduler, that can solve v...
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06/03/2021
Convergent Graph Solvers
We propose the convergent graph solver (CGS), a deep learning method tha...
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06/02/2021
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning
We propose a framework to learn to schedule a job-shop problem (JSSP) us...
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06/02/2021
A Hypergraph Convolutional Neural Network for Molecular Properties Prediction using Functional Group
We propose a Molecular Hypergraph Convolutional Network (MolHGCN) that p...
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01/18/2021
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
Training a multi-agent reinforcement learning (MARL) algorithm is more c...
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01/14/2021
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passi...
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09/20/2020
TorchDyn: A Neural Differential Equations Library
Continuous-depth learning has recently emerged as a novel perspective on...
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08/12/2020
REMAX: Relational Representation for Multi-Agent Exploration
Training a multi-agent reinforcement learning (MARL) model is generally ...
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07/19/2020
Hypersolvers: Toward Fast Continuous-Depth Models
The infinite-depth paradigm pioneered by Neural ODEs has launched a rena...
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06/12/2020
Approximate Inference for Spectral Mixture Kernel
A spectral mixture (SM) kernel is a flexible kernel used to model any st...
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03/18/2020
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential e...
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02/19/2020
Dissecting Neural ODEs
Continuous deep learning architectures have recently re-emerged as varia...
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01/07/2020
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission c...
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11/18/2019
Graph Neural Ordinary Differential Equations
We extend the framework of graph neural networks (GNN) to continuous tim...
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09/27/2019
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
Most previous studies on multi-agent reinforcement learning focus on der...
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09/24/2019
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Finance is a particularly challenging application area for deep learning...
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09/06/2019
Port-Hamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers a...
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05/29/2019
Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding
Can the success of reinforcement learning methods for simple combinatori...
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05/29/2019
Scalable and transferable learning of algorithms via graph embedding for multi-robot reward collection
Can the success of reinforcement learning methods for combinatorial opti...
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10/22/2018