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Langevin Quasi-Monte Carlo
Sampling from probability distributions is a crucial task in both statistics and machine learning. However, when the target distribution does not permit exact sampling, researchers often rely on Markov chain Monte Carlo (MCMC) methods.
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation Peihua Mai Ran Y an National University of Singapore Y an Pang
To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLP A) based on SecAgg protocol.
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability Ming Xiang
Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial memory/computation overhead.
In-ContextSymmetries: Self-Supervised LearningthroughContextualWorldModels
In this work, drawing insights from world models, we propose to instead learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention tocontext-- a memory module that tracks task-specificstates,actions,andfuturestates.
Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of
Logical Characterizations of Recurrent Graph Neural Networks with Reals and Floats
In pioneering work from 2019, Barceló and coauthors identified logics that precisely match the expressive power of constant iteration-depth graph neural networks (GNNs) relative to properties definable in first-order logic. In this article, we give exact logical characterizations of recurrent GNNs in two scenarios: (1) in the setting with floating-point numbers and (2) with reals. For floats, the formalism matching recurrent GNNs is a rule-based modal logic with counting, while for reals we use a suitable infinitary modal logic, also with counting. These results give exact matches between logics and GNNs in the recurrent setting without rel-ativising to a background logic in either case, but using some natural assumptions about floating-point arithmetic. Applying our characterizations, we also prove that, relative to graph properties definable in monadic second-order logic (MSO), our infinitary and rule-based logics are equally expressive. This implies that recurrent GNNs with reals and floats have the same expressive power over MSO-definable properties and shows that, for such properties, also recurrent GNNs with reals are characterized by a (finitary!)
Appendix for "Episodic Multi-Task Learning with Heterogeneous Neural Processes "
Appendix for "Episodic Multi-T ask Learning with Heterogeneous Neural Processes" In this section, we list frequently asked questions from researchers who help proofread this manuscript. As shown in Table 1, we use "Heterogeneous tasks" to distinguish the different branches of multi-task Meanwhile, "Episodic training" is used to describe the data-feeding strategy. Thus, "Heterogeneous tasks" is not available here (-). In episodic multi-task learning, we restrict the scope of the problem to the case where tasks in the same episode are related and share the same target space. This also implies that tasks with the same target space are related.
