Papers
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias
Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun et al.
Global Ground Metric Learning with Applications to scRNA data
Damin Kühn, Michael T Schaub
Global Group Fairness in Federated Learning via Function Tracking
Yves Rychener, Daniel Kuhn, Yifan Hu
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Yilin Xie, Shiqiang Zhang, Joel Paulson et al.
Graph-based Complexity for Causal Effect by Empirical Plug-in
Rina Dechter, Anna K Raichev, Jin Tian et al.
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen et al.
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
Xin Liu, Weijia Zhang, Min-Ling Zhang
HAR-former: Hybrid Transformer with an Adaptive Time-Frequency Representation Matrix for Long-Term Series Forecasting
Kenghao Zheng, Zi Long, Shuxin Wang
Harnessing Causality in Reinforcement Learning with Bagged Decision Times
Daiqi Gao, Hsin-Yu Lai, Predrag Klasnja et al.
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift
Mitsuhiro Fujikawa, Youhei Akimoto, Jun Sakuma et al.
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search
Tuan Nguyen, Jay Barrett, Kwang-Sung Jun
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
Keyue Jiang, Bohan Tang, Xiaowen Dong et al.
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani et al.
High Dimensional Bayesian Optimization using Lasso Variable Selection
Vu Viet Hoang, Hung The Tran, Sunil Gupta et al.
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
Daniel James Williams, Leyang Wang, Qizhen Ying et al.
High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise
Aleksandar Armacki, Shuhua Yu, Pranay Sharma et al.
How Well Can Transformers Emulate In-Context Newton’s Method?
Angeliki Giannou, Liu Yang, Tianhao Wang et al.
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
Chengrui Qu, Laixi Shi, Kishan Panaganti et al.
Hyperbolic Prototypical Entailment Cones for Image Classification
Samuele Fonio, Roberto Esposito, Marco Aldinucci
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa et al.
Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchy
Roman Malashin, Yachnaya Valeria, Alexandr V. Mullin
Implicit Diffusion: Efficient optimization through stochastic sampling
Pierre Marion, Anna Korba, Peter Bartlett et al.
Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi et al.