1 code implementation • 22 Mar 2024 • James Flemings, Meisam Razaviyayn, Murali Annavaram

Ensuring the privacy of Large Language Models (LLMs) is becoming increasingly important.

no code implementations • 28 Jan 2024 • Yinbin Han, Meisam Razaviyayn, Renyuan Xu

Our analysis is grounded in a novel parametric form of the neural network and an innovative connection between score matching and regression analysis, facilitating the application of advanced statistical and optimization techniques.

1 code implementation • 6 Dec 2023 • Sina Baharlouei, Shivam Patel, Meisam Razaviyayn

While numerous constraints and regularization terms have been proposed in the literature to promote fairness in machine learning tasks, most of these methods are not amenable to stochastic optimization due to the complex and nonlinear structure of constraints and regularizers.

no code implementations • 20 Sep 2023 • Sina Baharlouei, Meisam Razaviyayn

While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions.

1 code implementation • 26 Jun 2023 • Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

We show that the optimal error rates can be attained (up to log factors) by either discarding private data and training a public model, or treating public data like it is private and using an optimal DP algorithm.

no code implementations • 23 Jun 2023 • Daniel Lundstrom, Meisam Razaviyayn

Deep neural networks have produced significant progress among machine learning models in terms of accuracy and functionality, but their inner workings are still largely unknown.

no code implementations • 4 May 2023 • Daniel Lundstrom, Meisam Razaviyayn

We show that, given modest assumptions, a unique full account of interactions between features, called synergies, is possible in the continuous input setting.

no code implementations • 15 Mar 2023 • Yinbin Han, Meisam Razaviyayn, Renyuan Xu

Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications.

1 code implementation • 26 Oct 2022 • Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter

This work concerns the development of deep networks that are certifiably robust to adversarial attacks.

1 code implementation • 17 Oct 2022 • Andrew Lowy, Devansh Gupta, Meisam Razaviyayn

However, existing algorithms for DP fair learning are either not guaranteed to converge or require full batch of data in each iteration of the algorithm to converge.

no code implementations • 24 Sep 2022 • Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanović

We study momentum-based first-order optimization algorithms in which the iterations utilize information from the two previous steps and are subject to an additive white noise.

no code implementations • 15 Sep 2022 • Andrew Lowy, Meisam Razaviyayn

To address these limitations, this work provides near-optimal excess risk bounds that do not depend on the uniform Lipschitz parameter of the loss.

1 code implementation • 13 Mar 2022 • Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn

silo data and two classes of Lipschitz continuous loss functions: First, we consider losses satisfying the Proximal Polyak-Lojasiewicz (PL) inequality, which is an extension of the classical PL condition to the constrained setting.

1 code implementation • 24 Feb 2022 • Daniel Lundstrom, Tianjian Huang, Meisam Razaviyayn

Attribution methods address the issue of explainability by quantifying the importance of an input feature for a model prediction.

1 code implementation • 21 Oct 2021 • Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.

Ranked #1 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.2

Multi-domain Dialogue State Tracking Visual Question Answering

no code implementations • 8 Oct 2021 • Dmitrii M. Ostrovskii, Babak Barazandeh, Meisam Razaviyayn

For $0 \le k \le 2$ the surrogate function can be efficiently maximized in $y$; our general approximation result then leads to efficient algorithms for finding a near-stationary point in nonconvex-nonconcave min-max problems, for which we also provide convergence guarantees.

1 code implementation • 1 Sep 2021 • Sina Baharlouei, Kelechi Ogudu, Sze-chuan Suen, Meisam Razaviyayn

We develop a statistical inference framework for regression and classification in the presence of missing data without imputation.

2 code implementations • 17 Jun 2021 • Andrew Lowy, Meisam Razaviyayn

This paper studies federated learning (FL)--especially cross-silo FL--with data from people who do not trust the server or other silos.

no code implementations • 12 May 2021 • Babak Barazandeh, Ali Ghafelebashi, Meisam Razaviyayn, Ram Sriharsha

When the additive noise in MLR model is Gaussian, Expectation-Maximization (EM) algorithm is a widely-used algorithm for maximum likelihood estimation of MLR parameters.

1 code implementation • NeurIPS 2021 • Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

We consider the problem of fair classification with discrete sensitive attributes and potentially large models and data sets, requiring stochastic solvers.

no code implementations • 9 Feb 2021 • Andrew Lowy, Meisam Razaviyayn

Finally, we apply our theory to two learning frameworks: tilted ERM and adversarial learning.

no code implementations • 1 Jan 2021 • Rakesh Pavan, Andrew Lowy, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami

In this paper, we propose another notion of fairness violation, called Exponential Rényi Mutual Information (ERMI) between sensitive attributes and the predicted target.

1 code implementation • 4 Dec 2020 • Dmitrii M. Ostrovskii, Mohamed Ndaoud, Adel Javanmard, Meisam Razaviyayn

Here we provide matching upper and lower bounds on the sample complexity as given by $\min\{1/\Delta^2,\sqrt{r}/\Delta\}$ up to a constant factor; here $\Delta$ is a measure of separation between $\mathbb{P}_0$ and $\mathbb{P}_1$ and $r$ is the rank of the design covariance matrix.

no code implementations • NeurIPS 2020 • Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong

To the best of our knowledge, this is the first time that first-order algorithms with polynomial per-iteration complexity and global sublinear rate are designed to find SOSPs of the important class of non-convex problems with linear constraints (almost surely).

no code implementations • 8 Sep 2020 • Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn

For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for Quantization ($\texttt{ADMM-Q}$) algorithm, which is a variant of the widely-used ADMM method applied to our discrete optimization problem.

no code implementations • 15 Jun 2020 • Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong

The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games.

no code implementations • 18 Mar 2020 • Babak Barazandeh, Meisam Razaviyayn

Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing.

no code implementations • 18 Feb 2020 • Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn

As a byproduct, the choice $\varepsilon_y = O(\varepsilon_x{}^2)$ allows for the $O(\varepsilon_x{}^{-3})$ complexity of finding an $\varepsilon_x$-stationary point for the standard Moreau envelope of the primal function.

Optimization and Control 90C06, 90C25, 90C26, 91A99

no code implementations • 22 Jan 2020 • Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn

We establish that under the SGC assumption, the complexities of the stochastic algorithms match that of deterministic algorithms.

no code implementations • 22 Nov 2019 • Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn, Jason D. Lee

We study the optimization problem for decomposing $d$ dimensional fourth-order Tensors with $k$ non-orthogonal components.

no code implementations • 9 Jul 2019 • Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong

This paper proposes low-complexity algorithms for finding approximate second-order stationary points (SOSPs) of problems with smooth non-convex objective and linear constraints.

no code implementations • ICLR 2020 • Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn

In this paper, we use R\'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness.

no code implementations • 27 May 2019 • Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanović

We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation.

2 code implementations • 22 Apr 2019 • Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi

This design helps us to avoid the min-max formulation and leads to an optimization problem that is stable and could be solved efficiently.

1 code implementation • NeurIPS 2019 • Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn

In this paper, we study the problem in the non-convex regime and show that an \varepsilon--first order stationary point of the game can be computed when one of the player's objective can be optimized to global optimality efficiently.

no code implementations • 7 Dec 2018 • Maziar Sanjabi, Meisam Razaviyayn, Jason D. Lee

In this short note, we consider the problem of solving a min-max zero-sum game.

no code implementations • 24 Sep 2018 • Babak Barazandeh, Meisam Razaviyayn

Our numerical experiments show that our algorithm outperforms the Naive EM algorithm in almost all scenarios.

no code implementations • ICML 2018 • Mingyi Hong, Meisam Razaviyayn, Jason Lee

In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems.

no code implementations • NeurIPS 2018 • Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee

A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions.

no code implementations • NeurIPS 2017 • Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh

Such bias is measured by the cross validation procedure in practice where the data set is partitioned into a training set used for training and a validation set, which is not used in training and is left to measure the out-of-sample performance.

no code implementations • NeurIPS 2015 • Meisam Razaviyayn, Farzan Farnia, David Tse

We prove that for a given set of marginals, the minimum Hirschfeld-Gebelein-Renyi (HGR) correlation principle introduced in [1] leads to a randomized classification rule which is shown to have a misclassification rate no larger than twice the misclassification rate of the optimal classifier.

no code implementations • 5 Nov 2015 • Meisam Razaviyayn, Hung-Wei Tseng, Zhi-Quan Luo

In this paper we consider the dictionary learning problem for sparse representation.

1 code implementation • NeurIPS 2014 • Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang

In this work, we propose an inexact parallel BCD approach where at each iteration, a subset of the variables is updated in parallel by minimizing convex approximations of the original objective function.

Optimization and Control

no code implementations • 11 Sep 2012 • Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo

The block coordinate descent (BCD) method is widely used for minimizing a continuous function f of several block variables.

Optimization and Control

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