Papers
$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization
Jiaqi Han, Mingjian Jiang, Yuxuan Song et al.
$β$-th order Acyclicity Derivatives for DAG Learning
Madhumitha Shridharan, Garud Iyengar
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
Ossi Räisä, Antti Honkela
A Causal Framework for Evaluating Deferring Systems
Filippo Palomba, Andrea Pugnana, Jose Manuel Alvarez et al.
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
David Martínez-Rubio, Christophe Roux, Christopher Criscitiello et al.
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
Amartya Sanyal, Yaxi Hu, Yaodong Yu et al.
Achieving $\widetilde\mathcalO(\sqrtT)$ Regret in Average-Reward POMDPs with Known Observation Models
Alessio Russo, Alberto Maria Metelli, Marcello Restelli
A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset
Beomjun Kim, Jaehwan Kim, Kangyeon Kim et al.
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
Uday Kiran Reddy Tadipatri, Benjamin David Haeffele, Joshua Agterberg et al.
Active Bipartite Ranking with Smooth Posterior Distributions
James Cheshire, Stephan Clémençon
Active Feature Acquisition for Personalised Treatment Assignment
Julianna Piskorz, Nicolás Astorga, Jeroen Berrevoets et al.
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang et al.
Adaptive Convergence Rates for Log-Concave Maximum Likelihood
Gil Kur, Aditya Guntuboyina
Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions
Yang Luo, Michael J O’Neill
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A Álvarez
Additive Model Boosting: New Insights and Path(ologie)s
Rickmer Schulte, David Rügamer
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Kaan Ozkara, Bruce Huang, Ruida Zhou et al.
A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis
Wenjing Han, Yueming Wu, Xinwei Sun et al.
Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
Hongni Wang, Junxi Zhang, Na Li et al.
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits
Sreejeet Maity, Aritra Mitra
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno et al.
A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
Takahiro Kawashima, Hideitsu Hino
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
Chungpa Lee, Jongho Im, Joseph H.T. Kim
A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions
Danial Davarnia, Mohammadreza Kiaghadi