
Learning to Optimize: A Primer and A Benchmark
Learning to optimize (L2O) is an emerging approach that leverages machin...
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Robust Reinforcement Learning for General Video Game Playing
Reinforcement learning has successfully learned to play challenging boar...
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Deep Learning for Procedural Content Generation
Procedural content generation in video games has a long history. Existin...
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Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Conducting text retrieval in a dense learned representation space has ma...
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A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
The reliable facility location problem (RFLP) is an important research t...
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Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
The dream of machine learning in materials science is for a model to lea...
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Towards instore multiperson tracking using head detection and track heatmaps
Computer vision algorithms are being implemented across a breadth of ind...
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A Novel CNetassisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
Applying latent variable evolution to game level design has become more ...
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Versatile BlackBox Optimization
Choosing automatically the right algorithm using problem descriptors is ...
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Interactive Evolution and Exploration Within Latent LevelDesign Space of Generative Adversarial Networks
Generative Adversarial Networks (GANs) are an emerging form of indirect ...
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Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
Interferometric phase restoration has been investigated for decades and ...
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Decoder Choice Network for MetaLearning
Metalearning has been widely used for implementing fewshot learning an...
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Stock Prices Prediction using Deep Learning Models
Financial markets have a vital role in the development of modern society...
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Gradient Boost with Convolution Neural Network for Stock Forecast
Market economy closely connects aspects to all walks of life. The stock ...
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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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PlugandPlay Methods Provably Converge with Properly Trained Denoisers
Plugandplay (PnP) is a nonconvex framework that integrates modern den...
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Algorithm Portfolio for Individualbased SurrogateAssisted Evolutionary Algorithms
Surrogateassisted evolutionary algorithms (SAEAs) are powerful optimisa...
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Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Gamebased benchmarks have been playing an essential role in the develop...
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Voronoibased Efficient Surrogateassisted Evolutionary Algorithm for Very Expensive Problems
Very expensive problems are very common in practical system that one fit...
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Efficient Evolutionary Methods for Game Agent Optimisation: ModelBased is Best
This paper introduces a simple and fast variant of Planet Wars as a test...
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Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Machine learning workflow development is a process of trialanderror: d...
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Multilevel Optimal Transport: a Fast Approximation of Wasserstein1 distances
We propose a fast algorithm for the calculation of the Wasserstein1 dis...
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Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
In recent years, unfolding iterative algorithms as neural networks has b...
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Helix: Accelerating Humanintheloop Machine Learning
Data application developers and data scientists spend an inordinate amou...
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Deep Reinforcement Learning for General Video Game AI
The General Video Game AI (GVGAI) competition and its associated softwar...
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Shallow decisionmaking analysis in General Video Game Playing
The General Video Game AI competitions have been the testing ground for ...
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Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Generative Adversarial Networks (GANs) are a machine learning approach c...
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Accelerating Humanintheloop Machine Learning: Challenges and Opportunities
Development of machine learning (ML) workflows is a tedious process of i...
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General Video Game AI: a MultiTrack Framework for Evaluating Agents, Games and Content Generation Algorithms
General Video Game Playing (GVGP) aims at designing an agent that is cap...
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PSObased Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application
This paper proposes an agent with particle swarm optimization (PSO) base...
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The NTuple Bandit Evolutionary Algorithm for Game Agent Optimisation
This paper describes the NTuple Bandit Evolutionary Algorithm (NTBEA), ...
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Online Convolutional Dictionary Learning
Convolutional sparse representations are a form of sparse representation...
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Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm
The compact genetic algorithm is an Estimation of Distribution Algorithm...
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Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
A key part of any evolutionary algorithm is fitness evaluation. When fit...
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Performance Evaluation and Modeling of HPC I/O on NonVolatile Memory
HPC applications pose high demands on I/O performance and storage capabi...
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Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
Monte Carlo Tree Search techniques have generally dominated General Vide...
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Automatically Reinforcing a Game AI
A recent research trend in Artificial Intelligence (AI) is the combinati...
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Optimal resampling for the noisy OneMax problem
The OneMax problem is a standard benchmark optimisation problem for a bi...
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Learning opening books in partially observable games: using random seeds in Phantom Go
Many artificial intelligences (AIs) are randomized. One can be lucky or ...
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Rolling Horizon Coevolutionary Planning for TwoPlayer Video Games
This paper describes a new algorithm for decision making in twoplayer r...
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BanditBased Random Mutation HillClimbing
The Random Mutation HillClimbing algorithm is a direct search technique...
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Random MultiConstraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Consider convex optimization problems subject to a large number of const...
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Jialin Liu
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Computer Engineer and Researcher at Lawrence Berkeley National Lab