3 citations · 4 across the 5 of their papers we have counts for
5 papers
Merge to Mix: Mixing Datasets via Model Merging
Zhixu Silvia Tao, Kasper Vinken, Hao-Wei Yeh +2
Mixing datasets for fine-tuning large models (LMs) has become critical for maximizing performance on downstream tasks. However, composing effective dataset mixtures typically relie…
Task Arithmetic Through The Lens Of One-Shot Federated Learning
Zhixu Silvia Tao, Ian Mason, Sanjeev Kulkarni +1
Task Arithmetic is a model merging technique that enables the combination of multiple models' capabilities into a single model through simple arithmetic in the weight space, withou…
Fast convergence of a Federated Expectation-Maximization Algorithm
Zhixu Tao, Rajita Chandak, Sanjeev Kulkarni
Data heterogeneity has been a long-standing bottleneck in studying the convergence rates of Federated Learning algorithms. In order to better understand the issue of data heterogen…
Byzantine-Robust Clustered Federated Learning
Zhixu Tao, Kun Yang, Sanjeev R. Kulkarni
This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clust…
Projections with logarithmic divergences
Zhixu Tao, Ting-Kam Leonard Wong
In information geometry, generalized exponential families and statistical manifolds with curvature are under active investigation in recent years. In this paper we consider the sta…