Search Results for author: Panos Toulis

Found 10 papers, 4 papers with code

Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum

no code implementations11 Nov 2021 Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis

Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network training.

Robust Inference for High-Dimensional Linear Models via Residual Randomization

1 code implementation14 Jun 2021 Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar

We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting.

Vocal Bursts Intensity Prediction

Invariant Inference via Residual Randomization

1 code implementation12 Aug 2019 Panos Toulis

We find that invariant inference via residual randomization has three appealing properties: (1) It is valid under weak and interpretable conditions, allowing for problems with heavy-tailed data, finite clustering, and even some high-dimensional settings.

Dynamical systems theory for causal inference with application to synthetic control methods

1 code implementation27 Aug 2018 Yi Ding, Panos Toulis

In this setting, we propose to screen out control units that have a weak dynamical relationship to the single treated unit before the model is fit.


Convergence diagnostics for stochastic gradient descent with constant step size

no code implementations17 Oct 2017 Jerry Chee, Panos Toulis

During the transient phase the procedure converges towards a region of interest, and during the stationary phase the procedure oscillates in that region, commonly around a single point.

Stochastic Optimization

The Proximal Robbins-Monro Method

no code implementations4 Oct 2015 Panos Toulis, Thibaut Horel, Edoardo M. Airoldi

Exact implementations of the proximal Robbins-Monro procedure are challenging, but we show that approximate implementations lead to procedures that are easy to implement, and still dominate classical procedures by achieving numerical stability, practically without tradeoffs.

Stochastic Optimization

Stochastic gradient descent methods for estimation with large data sets

1 code implementation22 Sep 2015 Dustin Tran, Panos Toulis, Edoardo M. Airoldi

When the update is based on a noisy gradient, the stochastic approximation is known as standard stochastic gradient descent, which has been fundamental in modern applications with large data sets.

Towards stability and optimality in stochastic gradient descent

no code implementations10 May 2015 Panos Toulis, Dustin Tran, Edoardo M. Airoldi

For statistical efficiency, AI-SGD employs averaging of the iterates, which achieves the optimal Cram\'{e}r-Rao bound under strong convexity, i. e., it is an optimal unbiased estimator of the true parameter value.

Implicit Temporal Differences

no code implementations21 Dec 2014 Aviv Tamar, Panos Toulis, Shie Mannor, Edoardo M. Airoldi

In reinforcement learning, the TD($\lambda$) algorithm is a fundamental policy evaluation method with an efficient online implementation that is suitable for large-scale problems.

Asymptotic and finite-sample properties of estimators based on stochastic gradients

no code implementations13 Aug 2014 Panos Toulis, Edoardo M. Airoldi

Here, we introduce implicit stochastic gradient descent procedures, which involve parameter updates that are implicitly defined.

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