Statistical Learning

Crystallization Learning with the Delaunay Triangulation ICML 2021 Data and Parameter Scaling Laws for Neural Machine Translation EMNLP 2021 Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach ICLR 2021 Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings NeurIPS 2021 Deeply-Debiased Off-Policy Interval Estimation ICML 2021 DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis AAAI 2021 Deep Reinforcement Learning at the Edge of the Statistical Precipice NeurIPS 2021 Defense-Friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty WACV 2021 Delving into Deep Imbalanced Regression ICML 2021 Density of States Estimation for Out of Distribution Detection AISTATS 2021 Detecting Rewards Deterioration in Episodic Reinforcement Learning ICML 2021 Detection of Signal in the Spiked Rectangular Models ICML 2021 Deterministic Finite-Memory Bias Estimation COLT 2021 DIBS: Diversity Inducing Information Bottleneck in Model Ensembles AAAI 2021 Differentially Private Assouad, Fano, and Le Cam ALT 2021 Differentially Private Quantiles ICML 2021 Differentially Private Sampling from Distributions NeurIPS 2021 Diffusion Source Identification on Networks with Statistical Confidence ICML 2021 Dimension reduction for data with heterogeneous missingness UAI 2021 Disambiguatory Signals are Stronger in Word-initial Positions EACL 2021 Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition NeurIPS 2021 Distribution Alignment: A Unified Framework for Long-Tail Visual Recognition CVPR 2021 Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting ICML 2021 Distribution-free inference for regression: discrete, continuous, and in between NeurIPS 2021 Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals NeurIPS 2021