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Found 21 papers, 1 papers with code
Rethinking Scaling Laws for Learning in Strategic Environments
We find that strategic interactions can break the conventional view of scaling laws$\unicode{x2013}$meaning that performance does not necessarily monotonically improve as models get larger and/ or more expressive (even with infinite data).
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games
Specifically, through a change of variable, we show that the update equation of the slow-timescale iterates resembles the classical smoothed best-response dynamics, where the regularized Nash gap serves as a valid Lyapunov function.
Algorithmic Collective Action in Machine Learning
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms.
Follower Agnostic Methods for Stackelberg Games
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner.
Langevin Monte Carlo for Contextual Bandits
Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i. e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices.
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
We study the problem of online learning in competitive settings in the context of two-sided matching markets.
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
For the class of nonconvex-PL zero-sum games, we exploit timescale separation to construct a potential function that when combined with the stability characterization and an asymptotic saddle avoidance result gives a global asymptotic almost-sure convergence guarantee to a set of the strict local minmax equilibrium.
Who Leads and Who Follows in Strategic Classification?
In particular, by generalizing the standard model to allow both players to learn over time, we show that a decision-maker that makes updates faster than the agents can reverse the order of play, meaning that the agents lead and the decision-maker follows.
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data.
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
For the class of nonconvex-PL zero-sum games, we exploit timescale separation to construct a potential function that when combined with the stability characterization and an asymptotic saddle avoidance result gives a global asymptotic almost-sure convergence guarantee to a set of the strict local minmax equilibrium.
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
Distributionally robust supervised learning (DRSL) is emerging as a key paradigm for building reliable machine learning systems for real-world applications -- reflecting the need for classifiers and predictive models that are robust to the distribution shifts that arise from phenomena such as selection bias or nonstationarity.
Expert Selection in High-Dimensional Markov Decision Processes
In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings.
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules.
On Thompson Sampling with Langevin Algorithms
The resulting approximate Thompson sampling algorithm has logarithmic regret and its computational complexity does not scale with the time horizon of the algorithm.
Feedback Linearization for Unknown Systems via Reinforcement Learning
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games
In such games the state and action spaces are continuous and global Nash equilibria can be found be solving coupled Ricatti equations.
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide local convergence guarantees to a neighborhood of a stable local Nash equilibrium.
On Gradient-Based Learning in Continuous Games
We formulate a general framework for competitive gradient-based learning that encompasses a wide breadth of multi-agent learning algorithms, and analyze the limiting behavior of competitive gradient-based learning algorithms using dynamical systems theory.
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes
We formulate a multi-armed bandit (MAB) approach to choosing expert policies online in Markov decision processes (MDPs).
Systems and Control
Inverse Risk-Sensitive Reinforcement Learning
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive.
