8 papers
Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models
Dung Le, Nicola Bariletto, Alessandro Rinaldo +1
We study identifiability of mixing measures in infinite mixture models. We show that, in many common cases, lack of identifiability can be characterized in terms of certain differe…
Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Nicola Bariletto, Dung Le, Alessandro Rinaldo +1
We study posterior contraction rates for mixing measures in homoscedastic location-scale mixture models with infinitely many components. While posterior convergence at the level of…
On the Geometry of Separation in Finite Gaussian Mixtures
Huy Nguyen, Dung Le, Alessandro Rinaldo +1
We study an open problem of understanding the effects of the minimum component separation on the convergence rates of parameter estimation in finite Gaussian mixtures. We address t…
The Confidence Trap: Calibration Attacks for Graph Neural Networks
Cuong Dang, Jiahao Zhang, Hieu Ta Quang +3
While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations…
Improving Minimax Estimation Rates for Contaminated Mixture of Multinomial Logistic Experts via Expert Heterogeneity
Fanqi Yan, Dung Le, Trang Pham +2
Contaminated mixture of experts (MoE) is motivated by transfer learning methods where a pre-trained model, acting as a frozen expert, is integrated with an adapter model, functioni…
Hypernetwork-Driven Low-Rank Adaptation Across Attention Heads
Nghiem T. Diep, Dung Le, Tuan Truong +3
Parameter-efficient fine-tuning (PEFT) has emerged as a powerful paradigm for adapting large-scale pre-trained models to downstream tasks with minimal additional parameters. Among…