Search Results for author: Tetiana Parshakova

Found 6 papers, 6 papers with code

Optimization Algorithm Design via Electric Circuits

1 code implementation4 Nov 2024 Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh

We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits.

Distributed Optimization

Fitting Multilevel Factor Models

1 code implementation18 Sep 2024 Tetiana Parshakova, Trevor Hastie, Stephen Boyd

We show that the inverse of an invertible PSD MLR matrix is also an MLR matrix with the same sparsity in factors, and we use the recursive Sherman-Morrison-Woodbury matrix identity to obtain the factors of the inverse.

Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices

2 code implementations30 Oct 2023 Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd

The second is rank allocation, where we choose the ranks of the blocks in each level, subject to the total rank having a given value, which preserves the total storage needed for the MLR matrix.

Distributional Reinforcement Learning for Energy-Based Sequential Models

1 code implementation18 Dec 2019 Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman

Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models.

Distributional Reinforcement Learning reinforcement-learning +2

Global Autoregressive Models for Data-Efficient Sequence Learning

1 code implementation CONLL 2019 Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman

In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the \emph{normalized} distribution associated with the GAM, and can be used for fast inference and evaluation.

Language Modelling Small Data Image Classification

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