A new class of latency functions is introduced to model congestion due to the formation of physical queues, inspired from the fundamental diagram of traffic. We also prove a general lower bound on the worst-case regret for any online algorithm. We consider a model in which players use regret-minimizing algorithms as the learning mechanism, and study the resulting dynamics. Benjamin received the Grand Prix d’option of Ecole Polytechnique. We show that the gradient of this term can be efficiently computed by maintaining estimates of the Gramians, and develop variance reduction schemes to improve the quality of the estimates. I did my Ph.
In order to demonstrate our methods, we developed a web application that allows players to participate in a distributed, online routing game, and we deployed the application on Amazon Mechanical Turk. Optimization Methods for Finance. We show that it can be guaranteed for a class of algorithms with a sublinear discounted regret and which satisfy an additional condition. Her research interests focus on using data to find insights that can be turned into learning interventions. Online Learning and Optimization: We consider an online learning model of player dynamics: Classes are growing in size and adding more and more technology.
Importantly, we make no convexity assumptions on either the set S or the reward functions. Continuous-time dynamics for stochastic optimization.
My thesis was on Continuous and discrete time dynamics for online learning and convex optimization. In order to demonstrate our methods, we developed a web application that allows players to participate in a distributed, online routing game, and we deployed the application disseetation Amazon Mechanical Turk.
This work is applied to modeling and simulating congestion relief on transportation networks, in which a coordinator traffic management agency can choose to route a fraction of compliant drivers, while the rest of the drivers choose their routes selfishly.
By studying the spectrum of the linearized system around rest points, we show that Nash equilibria are locally asymptotically stable stationary points. Many algorithms for online learning and convex optimization can be interpreted as a discretization of a continuous time process, and studying the continuous time dynamics offers many advantages: Some time before that I interned at National Instruments.
By varying the repetition times TR accross the different echo trains, T1 sensitivity is encoded in the imaging data. We investigate the following question: Classes are growing in size and adding more and more technology. I like working with undergraduates on interesting projects.
In particular, we show how the asymptotic rate of berke,ey affects the choice of parameters and, ultimately, the convergence rate. However, rather than seeing this as a problem, I believe scale can help classes. I was awarded the Leon O.
We are concerned in particular with the convergence to the set of Nash equilibria of the routing game. These results provide a distributed learning model that is robust to measurement noise and other stochastic perturbations, and allows flexibility in the choice of learning algorithm of each player. A new class of latency functions is introduced to model congestion due to the formation of physical queues, inspired from the fundamental diagram of traffic.
Distributed Learning in Routing Games: We disserattion new efficient methods to train these models without having to sample unobserved pairs.
In particular, the discounted Hedge algorithm is proved to belong eecs this class, which guarantees its convergence. For this talm class, some results from the classical congestion games literature in which latency is assumed to be a nondecreasing function of the flow do not hold. The game is stochastic in that each player observes a stochastic vector, the conditional expectation of which is equal to the true loss almost surely.
In the Stackelberg routing game, a central authority leader is assumed to have control over a fraction of the flow on the network compliant flowand the remaining flow responds selfishly. You can find all the materials presented at the workshop, including quick installation steps and demo walkthroughs, here: In the first part of the thesis, we study online learning dynamics for a class of games called non-atomic convex potential games, eecss are used for example to model congestion in transportation and communication networks.
Kristin Stephens-Martinez is a Ph. Minimizing regret on reflexive Banach spaces and Nash equilibria in continuous zero-sum games. I did my Ph. We develop a method to design an ODE for the problem using an inverse Lyapunov argument: If the learning dynamics are heterogeneous, that is, different players use different learning rates, then the joint strategy converges to equilibrium in expectation, and we give upper bounds on the convergence rate.
I will teach the optimization and recommender systems courses at the Nassma Machine Learning summer school from Jun No-regret learning algorithms are known to guarantee convergence of a subsequence of population strategies.
This results in a unified framework to derive and analyze most known first-order methods, from gradient descent and mirror descent to their accelerated versions.
Krichene, and Itamar Rossen.