Papers
Improved Regret Bounds for Projection-free Bandit Convex Optimization
Dan Garber, Ben Kretzu
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Yuxuan Song, Ning Miao, Hao Zhou et al.
Imputation estimators for unnormalized models with missing data
Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Jan Stuehmer, Richard Turner, Sebastian Nowozin
Inference of Dynamic Graph Changes for Functional Connectome
Dingjue Ji, Junwei Lu, Yiliang Zhang et al.
Infinitely deep neural networks as diffusion processes
Stefano Peluchetti, Stefano Favaro
Integrals over Gaussians under Linear Domain Constraints
Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
Interpretable Companions for Black-Box Models
Danqing Pan, Tong Wang, Satoshi Hara
Interpretable Deep Gaussian Processes with Moments
Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao et al.
Invertible Generative Modeling using Linear Rational Splines
Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie
Ivy: Instrumental Variable Synthesis for Causal Inference
Zhaobin Kuang, Frederic Sala, Nimit Sohoni et al.
Kernel Conditional Density Operators
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus et al.
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
Langevin Monte Carlo without smoothness
Niladri Chatterji, Jelena Diakonikolas, Michael I. Jordan et al.
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis
Kaige Yang, Laura Toni, Xiaowen Dong
LdSM: Logarithm-depth Streaming Multi-label Decision Trees
Maryam Majzoubi, Anna Choromanska
Learnable Bernoulli Dropout for Bayesian Deep Learning
Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh et al.
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Zhaozhi Qian, Ahmed Alaa, Alexis Bellot et al.
Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification
Zhengjue Wang, Chaojie Wang, Hao Zhang et al.
Learning Entangled Single-Sample Distributions via Iterative Trimming
Hui Yuan, Yingyu Liang
Learning Fair Representations for Kernel Models
Zilong Tan, Samuel Yeom, Matt Fredrikson et al.
Learning Gaussian Graphical Models via Multiplicative Weights
Anamay Chaturvedi, Jonathan Scarlett
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
Hussein Hazimeh, Rahul Mazumder
Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters
Yuhao Wang, Uma Roy, Caroline Uhler
Learning in Gated Neural Networks
Ashok Makkuva, Sewoong Oh, Sreeram Kannan et al.