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Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
We propose a versatile framework that combines DEQ with off-the-shelf machine learning models for predicting Hamiltonians.
Simple, Scalable and Effective Clustering via One-Dimensional Projections
In large-scale applications, datasets often contain billions of high-dimensional points. Grouping similar data points into clusters is crucial for understanding and organizing datasets.
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
We further show the mismatched sampling paradox: A learner who knows the rewards distributions and samples from the correct posterior distribution can perform exponentially worse than a learner who does not know the rewards and simply samples from a well-chosen Gaussian posterior.
