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09/20/2023
Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework
While training fair machine learning models has been studied extensively...
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06/26/2023
Optimal Differentially Private Learning with Public Data
Differential Privacy (DP) ensures that training a machine learning model...
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06/23/2023
Four Axiomatic Characterizations of the Integrated Gradients Attribution Method
Deep neural networks have produced significant progress among machine le...
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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...
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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...
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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...
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10/17/2022
Stochastic Differentially Private and Fair Learning
Machine learning models are increasingly used in high-stakes decision-ma...
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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...
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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...
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03/13/2022
Private Non-Convex Federated Learning Without a Trusted Server
We study differentially private (DP) federated learning (FL) with non-co...
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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...
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10/21/2021
DAIR: Data Augmented Invariant Regularization
While deep learning through empirical risk minimization (ERM) has succee...
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10/08/2021
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain
We study the problem of finding approximate first-order stationary point...
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09/01/2021
RIFLE: Robust Inference from Low Order Marginals
The ubiquity of missing values in real-world datasets poses a challenge ...
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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...
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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...
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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...
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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...
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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...
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09/08/2020
Alternating Direction Method of Multipliers for Quantization
Quantization of the parameters of machine learning models, such as deep ...
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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...
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01/22/2020
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities
In this paper, we study zeroth-order algorithms for minimax optimization...
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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...
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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...
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06/28/2019
Rényi Fair Inference
Machine learning algorithms have been increasingly deployed in critical ...
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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...
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04/22/2019
Training generative networks using random discriminators
In recent years, Generative Adversarial Networks (GANs) have drawn a lot...
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12/21/2018
Computational RAM to Accelerate String Matching at Scale
Traditional Von Neumann computing is falling apart in the era of explodi...
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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...
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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...
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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...
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02/22/2018
Solving Approximate Wasserstein GANs to Stationarity
Generative Adversarial Networks (GANs) are one of the most practical str...
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01/11/2018
Minimax Optimality of Sign Test for Paired Heterogeneous Data
Comparing two groups under different conditions is ubiquitous in the bio...
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11/14/2017
On Optimal Generalizability in Parametric Learning
We consider the parametric learning problem, where the objective of the ...
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09/07/2017
A Non-volatile Near-Memory Read Mapping Accelerator
DNA sequencing entails the process of determining the precise physical o...
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11/05/2015
