362 citations · 491 across the 19 of their papers we have counts for
22 papers
Response Time Enhances Alignment with Heterogeneous Preferences
Federico Echenique, Alireza Fallah, Baihe Huang +1
Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that…
Signaling in Data Markets via Free Samples
Nivasini Ananthakrishnan, Alireza Fallah, Michael I. Jordan
We study a setting in which a data buyer seeks to estimate an unknown parameter by purchasing samples from one of K data sellers. Each seller has privately known data quality (e.g.…
High-Probability Bounds For Heterogeneous Local Differential Privacy
Maryam Aliakbarpour, Alireza Fallah, Swaha Roy +1
We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Depar…
The Statistical Fairness-Accuracy Frontier
Alireza Fallah, Michael I. Jordan, Annie Ulichney
We study fairness-accuracy tradeoffs when a single predictive model must serve multiple demographic groups. A useful tool for understanding this tradeoff is the fairness-accuracy (…
A General Framework for Estimating Preferences Using Response Time Data
Federico Echenique, Alireza Fallah, Michael I. Jordan
We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast ( for data points) con…
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade +2
Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, i…
Fair Allocation in Dynamic Mechanism Design
Alireza Fallah, Michael I. Jordan, Annie Ulichney
We consider a dynamic mechanism design problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of rounds. The auctioneer aims to m…
On Three-Layer Data Markets
Alireza Fallah, Michael I. Jordan, Ali Makhdoumi +1
We study a three-layer data market comprising users (data owners), platforms, and a data buyer. Each user benefits from platform services in exchange for data, incurring privacy lo…
The Limits of Price Discrimination Under Privacy Constraints
Alireza Fallah, Michael I. Jordan, Ali Makhdoumi +1
We study a producer's problem of selling a product to a continuum of privacy-conscious consumers, where the producer can implement third-degree price discrimination, offering diffe…
Contract Design With Safety Inspections
Alireza Fallah, Michael I. Jordan
We study the role of regulatory inspections in a contract design problem in which a principal interacts separately with multiple agents. Each agent's hidden action includes a dimen…
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms
Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian +1
We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal m…
Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah +2
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm w…
A Wasserstein Minimax Framework for Mixed Linear Regression
Theo Diamandis, Yonina C. Eldar, Alireza Fallah +2
Multi-modal distributions are commonly used to model clustered data in statistical learning tasks. In this paper, we consider the Mixed Linear Regression (MLR) problem. We propose…
Optimal adaptive testing for epidemic control: combining molecular and serology tests
D. Acemoglu, A. Fallah, A. Giometto +4
The COVID-19 crisis highlighted the importance of non-medical interventions, such as testing and isolation of infected individuals, in the control of epidemics. Here, we show how t…
An Optimal Multistage Stochastic Gradient Method for Minimax Problems
Alireza Fallah, Asuman Ozdaglar, Sarath Pattathil
In this paper, we study the minimax optimization problem in the smooth and strongly convex-strongly concave setting when we have access to noisy estimates of gradients. In particul…
Personalized Federated Learning: A Meta-Learning Approach
Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
In Federated Learning, we aim to train models across multiple computing units (users), while users can only communicate with a common central server, without exchanging their data…
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar +2
We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the obje…
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
We study the convergence of a class of gradient-based Model-Agnostic Meta-Learning (MAML) methods and characterize their overall complexity as well as their best achievable accurac…
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban +1
We study the problem of minimizing a strongly convex, smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is univ…
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban +1
We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent (GD) and accelerated gradient…
Sampling and Distortion Tradeoffs for Indirect Source Retrieval
Elaheh Mohammadi, Alireza Fallah, Farokh Marvasti
Consider a continuous signal that cannot be observed directly. Instead, one has access to multiple corrupted versions of the signal. The available corrupted signals are correlated…
Multidimensional Lévy White Noises in Weighted Besov Spaces
Julien Fageot, Alireza Fallah, Michael Unser
In this paper, we study the Besov regularity of d-dimensional Lévy white noises. More precisely, we describe new sample paths properties of a given white noise in terms of weighted…