Papers
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon et al.
Structure-adaptive Manifold Estimation
Nikita Puchkin, Vladimir Spokoiny
Structure Learning for Directed Trees
Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann et al.
Sufficient reductions in regression with mixed predictors
Efstathia Bura, Liliana Forzani, Rodrigo Garcia Arancibia et al.
Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang et al.
Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Tomojit Ghosh, Michael Kirby
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
William Fedus, Barret Zoph, Noam Shazeer
Testing Whether a Learning Procedure is Calibrated
Jon Cockayne, Matthew M. Graham, Chris J. Oates et al.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang et al.
The AIM and EM Algorithms for Learning from Coarse Data
Manfred Jaeger
The Correlation-assisted Missing Data Estimator
Timothy I. Cannings, Yingying Fan
The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures
Nir Weinberger, Guy Bresler
The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation
Ulrike Schneider, Patrick Tardivel
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Konstantinos Pantazis, Avanti Athreya, Jesus Arroyo et al.
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data
T. Tony Cai, Rong Ma
The Separation Capacity of Random Neural Networks
Sjoerd Dirksen, Martin Genzel, Laurent Jacques et al.
The Two-Sided Game of Googol
José Correa, Andrés Cristi, Boris Epstein et al.
The Weighted Generalised Covariance Measure
Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann
Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin, Yohann De Castro, Claire Lacour
Tianshou: A Highly Modularized Deep Reinforcement Learning Library
Jiayi Weng, Huayu Chen, Dong Yan et al.
tntorch: Tensor Network Learning with PyTorch
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
Toolbox for Multimodal Learn (scikit-multimodallearn)
Dominique Benielli, Baptiste Bauvin, Sokol Koço et al.
Topologically penalized regression on manifolds
Olympio Hacquard, Krishnakumar Balasubramanian, Gilles Blanchard et al.