
The Fast Kernel Transform
Kernel methods are a highly effective and widely used collection of mode...
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Materials Representation and Transfer Learning for MultiProperty Prediction
The adoption of machine learning in materials science has rapidly transf...
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Towards Deeper Deep Reinforcement Learning
In computer vision and natural language processing, innovations in model...
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LowPrecision Reinforcement Learning
Lowprecision training has become a popular approach to reduce computati...
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Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
There has been an increasing interest in harnessing deep learning to tac...
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Autonomous synthesis of metastable materials
Autonomous experimentation enabled by artificial intelligence (AI) offer...
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Deep Hurdle Networks for ZeroInflated MultiTarget Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distr...
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Solving Hard AI Planning Instances Using CurriculumDriven Deep Reinforcement Learning
Despite significant progress in general AI planning, certain domains rem...
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Deep Reasoning Networks: Thinking Fast and Slow
We introduce Deep Reasoning Networks (DRNets), an endtoend framework t...
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Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
In this work, we consider applying machine learning to the analysis and ...
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Bias Reduction via EndtoEnd Shift Learning: Application to Citizen Science
Citizen science projects are successful at gathering rich datasets for v...
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EndtoEnd Learning for the Deep Multivariate Probit Model
The multivariate probit model (MVP) is a popular classic model for study...
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MultiEntity Dependence Learning with Rich Context via Conditional Variational Autoencoder
MultiEntity Dependence Learning (MEDL) explores conditional correlation...
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XORSampling for Network Design with Correlated Stochastic Events
Many network optimization problems can be formulated as stochastic netwo...
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Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Arising from many applications at the intersection of decision making an...
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PhaseMapper: An AI Platform to Accelerate High Throughput Materials Discovery
HighThroughput materials discovery involves the rapid synthesis, measur...
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Variable Elimination in the Fourier Domain
The ability to represent complex high dimensional probability distributi...
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Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Many probabilistic inference tasks involve summations over exponentially...
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Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
Integration is affected by the curse of dimensionality and quickly becom...
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Algorithm Portfolio Design: Theory vs. Practice
Stochastic algorithms are among the best for solving computationally har...
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A Bayesian Approach to Tackling Hard Computational Problems
We are developing a general framework for using learned Bayesian models ...
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Uniform Solution Sampling Using a Constraint Solver As an Oracle
We consider the problem of sampling from solutions defined by a set of h...
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Playing games against nature: optimal policies for renewable resource allocation
In this paper we introduce a class of Markov decision processes that ari...
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Carla P. Gomes
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Professor of Computer Science and the director of the Institute for Computational Sustainability at Cornell University