3.6k citations · 4.3k across the 44 of their papers we have counts for
49 papers
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
Zhihan Xiong, Romain Camilleri, Maryam Fazel +2
We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a potentially non-stationary environment. Given a finite arm set $\mathcal{X}\subset\mat…
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Haozhe Jiang, Qiwen Cui, Zhihan Xiong +2
We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specific…
No-Regret Online Prediction with Strategic Experts
Omid Sadeghi, Maryam Fazel
We study a generalization of the online binary prediction with expert advice framework where at each round, the learner is allowed to pick experts from a pool of expe…
Stochastic Contextual Bandits with Long Horizon Rewards
Yuzhen Qin, Yingcong Li, Fabio Pasqualetti +2
The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons. This work takes a ste…
On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds
Zhaolin Ren, Yang Zheng, Maryam Fazel +1
The problem of controller reduction has a rich history in control theory. Yet, many questions remain open. In particular, there exist very few results on the order reduction of gen…
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Bin Hu, Kaiqing Zhang, Na Li +3
Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical…
Offline congestion games: How feedback type affects data coverage requirement
Haozhe Jiang, Qiwen Cui, Zhihan Xiong +2
This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games. The existing dataset coverage assumption in offline gener…
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel +1
Iterative optimization algorithms depend on access to information about the objective function. In a differentiable programming framework, this information, such as gradients, can…
Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback
Adhyyan Narang, Omid Sadeghi, Lillian J Ratliff +2
In the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives. This includes…
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff +2
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in t…
Learning in Congestion Games with Bandit Feedback
Qiwen Cui, Zhihan Xiong, Maryam Fazel +1
In this paper, we investigate Nash-regret minimization in congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a…
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments
Mitas Ray, Dmitriy Drusvyatskiy, Maryam Fazel +1
This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to…
Escaping High-order Saddles in Policy Optimization for Linear Quadratic Gaussian (LQG) Control
Yang Zheng, Yue Sun, Maryam Fazel +1
First order policy optimization has been widely used in reinforcement learning. It guarantees to find the optimal policy for the state-feedback linear quadratic regulator (LQR). Ho…
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel +1
A classical approach for solving discrete time nonlinear control on a finite horizon consists in repeatedly minimizing linear quadratic approximations of the original problem aroun…
System Identification via Nuclear Norm Regularization
Yue Sun, Samet Oymak, Maryam Fazel
This paper studies the problem of identifying low-order linear systems via Hankel nuclear norm regularization. Hankel regularization encourages the low-rankness of the Hankel matri…
Flat minima generalize for low-rank matrix recovery
Lijun Ding, Dmitriy Drusvyatskiy, Maryam Fazel +1
Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly im…
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy +2
Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new game theo…
Selective Sampling for Online Best-arm Identification
Romain Camilleri, Zhihan Xiong, Maryam Fazel +2
This work considers the problem of selective-sampling for best-arm identification. Given a set of potential options , a learner aims to compute with…
Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games
Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J Ratliff
The influential work of Bravo et al. 2018 shows that derivative free play in strongly monotone games has complexity , where is the target accura…
Fast First-Order Methods for Monotone Strongly DR-Submodular Maximization
Omid Sadeghi, Maryam Fazel
Continuous DR-submodular functions are a class of functions that satisfy the Diminishing Returns (DR) property, which implies that they are concave along non-negative directions. E…
Computing Lewis Weights to High Precision
Maryam Fazel, Yin Tat Lee, Swati Padmanabhan +1
We present an algorithm for computing approximate Lewis weights to high precision. Given a full-rank with and a scalar…
Improved Regret Bounds for Online Submodular Maximization
Omid Sadeghi, Prasanna Raut, Maryam Fazel
In this paper, we consider an online optimization problem over rounds where at each step , the algorithm chooses an action from the fixed convex and compact doma…
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak +1
Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from comm…
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Zhihan Xiong, Ruoqi Shen, Qiwen Cui +2
We study algorithms using randomized value functions for exploration in reinforcement learning. This type of algorithms enjoys appealing empirical performance. We show that when we…