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Statistically Efficient Off-Policy Policy Gradients
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Striving for simplicity and performance in off-policy DRL Output Normalization and Non-Uniform Sampling
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Structure Adaptive Algorithms for Stochastic Bandits
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Tails of Lipschitz Triangular Flows
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Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
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Transformer Hawkes Process
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A Bit More Bayesian Domain-Invariant Learning with Uncertainty
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A Free Lunch From ANN Towards Efficient, Accurate Spiking Neural Networks Calibration
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Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator
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Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
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Annealed Flow Transport Monte Carlo
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AutoSampling Search for Effective Data Sampling Schedules
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Bayesian Attention Belief Networks
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Bayesian Optimization over Hybrid Spaces
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Calibrate Before Use Improving Few-shot Performance of Language Models
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Conjugate Energy-Based Models
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Do We Actually Need Dense Over-Parameterization In-Time Over-Parameterization in Sparse Training
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CRPO A New Approach for Safe Reinforcement Learning with Convergence Guarantee
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Dataset Condensation with Differentiable Siamese Augmentation
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