Vitaly Aksenov

Georgii Semenov, Vitaly Aksenov

Digital collaboration systems support asynchronous work over replicated data, where conflicts arise when concurrent operations cannot be unambiguously integrated into a shared history. While Conflict-Free Replicated Data Types (CRDTs) ensure convergence through built-in conflict resolution, this resolution is typically implicit and opaque to users, whereas existing reconciliation techniques often rely on centralized coordination. This paper introduces a conflict model for collaborative data structures that enables explicit, local-first conflict resolution without central coordination. The model identifies conflicts using semantic dependencies between operations and resolves them by rebasing conflicting operations onto a reconciling operation via a three-way merge over a replicated journal. We demonstrate our approach on collaborative registers, including an explicit formulation of the Last-Writer-Wins Register and a multi-register entity supporting semi-automatic reconciliation.

Enrico Lopedoto, Maksim Shekhunov, Vitaly Aksenov et al.

In this work, we introduce a novel approach to regularization in multivariable regression problems. Our regularizer, called DLoss, penalises differences between the model's derivatives and derivatives of the data generating function as estimated from the training data. We call these estimated derivatives data derivatives. The goal of our method is to align the model to the data, not only in terms of target values but also in terms of the derivatives involved. To estimate data derivatives, we select (from the training data) 2-tuples of input-value pairs, using either nearest neighbour or random, selection. On synthetic and real datasets, we evaluate the effectiveness of adding DLoss, with different weights, to the standard mean squared error loss. The experimental results show that with DLoss (using nearest neighbour selection) we obtain, on average, the best rank with respect to MSE on validation data sets, compared to no regularization, L2 regularization, and Dropout.

Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen

The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been considerable effort invested into developing efficient parallel variants of classic machine learning algorithms. However, despite the wealth of knowledge on parallelization, some classic machine learning algorithms often prove hard to parallelize efficiently while maintaining convergence. In this paper, we focus on efficient parallel algorithms for the key machine learning task of inference on graphical models, in particular on the fundamental belief propagation algorithm. We address the challenge of efficiently parallelizing this classic paradigm by showing how to leverage scalable relaxed schedulers in this context. We present an extensive empirical study, showing that our approach outperforms previous parallel belief propagation implementations both in terms of scalability and in terms of wall-clock convergence time, on a range of practical applications.