Meisam Razaviyayn

research

∙ 09/20/2023

Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework

While training fair machine learning models has been studied extensively...

0 Sina Baharlouei, et al. ∙

research

∙ 06/26/2023

Optimal Differentially Private Learning with Public Data

Differential Privacy (DP) ensures that training a machine learning model...

0 Andrew Lowy, et al. ∙

research

∙ 06/23/2023

Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

Deep neural networks have produced significant progress among machine le...

0 Daniel Lundstrom, et al. ∙

research

∙ 05/04/2023

Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability

Deep learning has revolutionized many areas of machine learning, from co...

0 Daniel Lundstrom, et al. ∙

research

∙ 03/15/2023

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators

Nonlinear control systems with partial information to the decision maker...

0 Yinbin Han, et al. ∙

research

∙ 10/26/2022

Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes

This work concerns the development of deep networks that are certifiably...

0 Sina Baharlouei, et al. ∙

research

∙ 10/17/2022

Stochastic Differentially Private and Fair Learning

Machine learning models are increasingly used in high-stakes decision-ma...

0 Andrew Lowy, et al. ∙

research

∙ 09/24/2022

Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms

We study momentum-based first-order optimization algorithms in which the...

0 Hesameddin Mohammadi, et al. ∙

research

∙ 09/15/2022

Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses

We study differentially private (DP) stochastic optimization (SO) with d...

0 Andrew Lowy, et al. ∙

research

∙ 03/13/2022

Private Non-Convex Federated Learning Without a Trusted Server

We study differentially private (DP) federated learning (FL) with non-co...

0 Andrew Lowy, et al. ∙

research

∙ 02/24/2022

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

As the efficacy of deep learning (DL) grows, so do concerns about the la...

0 Daniel Lundstrom, et al. ∙

research

∙ 10/21/2021

DAIR: Data Augmented Invariant Regularization

While deep learning through empirical risk minimization (ERM) has succee...

0 Tianjian Huang, et al. ∙

research

∙ 10/08/2021

Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain

We study the problem of finding approximate first-order stationary point...

0 Dmitrii M. Ostrovskii, et al. ∙

research

∙ 09/01/2021

RIFLE: Robust Inference from Low Order Marginals

The ubiquity of missing values in real-world datasets poses a challenge ...

10 Sina Baharlouei, et al. ∙

research

∙ 06/17/2021

Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds

Federated learning (FL) is a distributed learning paradigm in which many...

0 Andrew Lowy, et al. ∙

research

∙ 05/12/2021

Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models

Mixed linear regression (MLR) model is among the most exemplary statisti...

0 Babak Barazandeh, et al. ∙

research

∙ 02/24/2021

FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information

In this paper, we propose a new notion of fairness violation, called Exp...

0 Andrew Lowy, et al. ∙

research

∙ 02/09/2021

Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training

Finding efficient, easily implementable differentially private (DP) algo...

0 Andrew Lowy, et al. ∙

research

∙ 12/04/2020

Near-Optimal Model Discrimination with Non-Disclosure

Let θ_0,θ_1 ∈ℝ^d be the population risk minimizers associated to some lo...

0 Dmitrii M. Ostrovskii, et al. ∙

research

∙ 09/08/2020

Alternating Direction Method of Multipliers for Quantization

Quantization of the parameters of machine learning models, such as deep ...

0 Tianjian Huang, et al. ∙

research

∙ 03/18/2020

Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method

Min-max saddle point games appear in a wide range of applications in mac...

0 Babak Barazandeh, et al. ∙

research

∙ 01/22/2020

Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities

In this paper, we study zeroth-order algorithms for minimax optimization...

0 Zhongruo Wang, et al. ∙

research

∙ 11/22/2019

When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?

We study the optimization problem for decomposing d dimensional fourth-o...

0 Maziar Sanjabi, et al. ∙

research

∙ 07/09/2019

SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems

This paper proposes low-complexity algorithms for finding approximate se...

0 Songtao Lu, et al. ∙

research

∙ 06/28/2019

Rényi Fair Inference

Machine learning algorithms have been increasingly deployed in critical ...

0 Sina Baharlouei, et al. ∙

research

∙ 05/27/2019

Robustness of accelerated first-order algorithms for strongly convex optimization problems

We study the robustness of accelerated first-order algorithms to stochas...

0 Hesameddin Mohammadi, et al. ∙

research

∙ 04/22/2019

Training generative networks using random discriminators

In recent years, Generative Adversarial Networks (GANs) have drawn a lot...

0 Babak Barazandeh, et al. ∙

research

∙ 12/21/2018

Computational RAM to Accelerate String Matching at Scale

Traditional Von Neumann computing is falling apart in the era of explodi...

0 Zamshed I. Chowdhury, et al. ∙

research

∙ 12/07/2018

Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition

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

0 Maziar Sanjabi, et al. ∙

research

∙ 09/24/2018

On the Behavior of the Expectation-Maximization Algorithm for Mixture Models

Finite mixture models are among the most popular statistical models used...

0 Babak Barazandeh, et al. ∙

research

∙ 02/25/2018

Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization

In this work, we study two first-order primal-dual based algorithms, the...

0 Mingyi Hong, et al. ∙

research

∙ 02/22/2018

Solving Approximate Wasserstein GANs to Stationarity

Generative Adversarial Networks (GANs) are one of the most practical str...

0 Maziar Sanjabi, et al. ∙

research

∙ 01/11/2018

Minimax Optimality of Sign Test for Paired Heterogeneous Data

Comparing two groups under different conditions is ubiquitous in the bio...

0 Martin J. Zhang, et al. ∙

research

∙ 11/14/2017

On Optimal Generalizability in Parametric Learning

We consider the parametric learning problem, where the objective of the ...

0 Ahmad Beirami, et al. ∙

research

∙ 09/07/2017

A Non-volatile Near-Memory Read Mapping Accelerator

DNA sequencing entails the process of determining the precise physical o...

0 S. Karen Khatamifard, et al. ∙

research

∙ 11/05/2015

Computational Intractability of Dictionary Learning for Sparse Representation

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

0 Meisam Razaviyayn, et al. ∙

Meisam Razaviyayn

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