1 citations · 2 across the 6 of their papers we have counts for
6 papers
Near-Optimal Fault Tolerance for Efficient Batch Matrix Multiplication via an Additive Combinatorics Lens
Keren Censor-Hillel, Yuka Machino, Pedro Soto
Fault tolerance is a major concern in distributed computational settings. In the classic master-worker setting, a server (the master) needs to perform some heavy computation which…
Robust Parameter Estimation for Rational Ordinary Differential Equations
Oren Bassik, Yosef Berman, Soo Go +6
We present a new approach for estimating parameters in rational ODE models from given (measured) time series data. In typical existing approaches, an initial guess for the paramete…
More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability
Ilia Ilmer, Alexey Ovchinnikov, Gleb Pogudin +1
Structural global parameter identifiability indicates whether one can determine a parameter's value from given inputs and outputs in the absence of noise. If a given model has para…
Faster Gröbner bases for Lie derivatives of ODE systems via monomial orderings
Mariya Bessonov, Ilia Ilmer, Tatiana Konstantinova +3
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial or rational ODE system w…
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
Pedro Soto
Tensors are a fundamental operation in distributed computing, \emph{e.g.,} machine learning, that are commonly distributed into multiple parallel tasks for large datasets. Straggle…
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Pedro Soto, Ilia Ilmer, Haibin Guan +1
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This paper proposes a novel algo…