Papers
Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns et al.
Differentially Private Sliced Wasserstein Distance
Alain Rakotomamonjy, Ralaivola Liva
Diffusion Earth Mover’s Distance and Distribution Embeddings
Alexander Y Tong, Guillaume Huguet, Amine Natik et al.
Diffusion Source Identification on Networks with Statistical Confidence
Quinlan E Dawkins, Tianxi Li, Haifeng Xu
Dimensionality Reduction for the Sum-of-Distances Metric
Zhili Feng, Praneeth Kacham, David Woodruff
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej L Wiatrak, Angus Brayne et al.
Directional Bias Amplification
Angelina Wang, Olga Russakovsky
Directional Graph Networks
Dominique Beaini, Saro Passaro, Vincent Létourneau et al.
Disambiguation of Weak Supervision leading to Exponential Convergence rates
Vivien A Cabannnes, Francis Bach, Alessandro Rudi
Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela, Brenden K Petersen, Sookyung Kim et al.
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
Changhun Jo, Kangwook Lee
Discretization Drift in Two-Player Games
Mihaela C Rosca, Yan Wu, Benoit Dherin et al.
Discriminative Complementary-Label Learning with Weighted Loss
Yi Gao, Min-Ling Zhang
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
Ankit Singh Rawat, Aditya K Menon, Wittawat Jitkrittum et al.
Disentangling syntax and semantics in the brain with deep networks
Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King
Dissecting Supervised Contrastive Learning
Florian Graf, Christoph Hofer, Marc Niethammer et al.
Distributed Nyström Kernel Learning with Communications
Rong Yin, Weiping Wang, Dan Meng
Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtarik
Distributionally Robust Optimization with Markovian Data
Mengmeng Li, Tobias Sutter, Daniel Kuhn
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
Chirag Gupta, Aaditya Ramdas
Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami et al.
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Seungyul Han, Youngchul Sung
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Yu Bai, Song Mei, Huan Wang et al.