BERKELEY EECS DISSERTATION TALK

Then, by carefully discretizing the ODE, we obtain a family of accelerated algorithms with optimal rate of convergence. No-regret learning algorithms are known to guarantee convergence of a subsequence of population strategies. We use a stochastic online learning framework for the population dynamics, which is known to converge to the Nash equilibrium of the routing game. By studying the spectrum of the linearized system around rest points, we show that Nash equilibria are locally asymptotically stable stationary points. We show that it can be guaranteed for a class of algorithms with a sublinear discounted regret and which satisfy an additional condition. We conduct large-scale experiments that show a significant improvement both in training time and generalization performance compared to sampling methods. A joint T1-T2 subspace is computed from an ensemble of simulated FSE signal evolutions, and linear combinations of the subspace coefficients are computed to generate synthetic T1-weighted and T2-weighted image contrasts.

Efficient Bregman Projections onto the Simplex. On the convergence of online learning in selfish routing. In this article, we consider a network of scalar conservation laws with general topology, whose behavior is modified by a set of control parameters in order to minimize a given objective function. The shuffling leads to reduced image blur at the cost of noise-like artifacts. We also use the estimated model parameters to predict the flow distribution over routes, and compare our predictions to the actual distributions, showing that the online learning model can be used as a predictive model over short horizons. The artifacts are iteratively suppressed in a regularized reconstruction based on compressed sensing, and the full signal dynamics are recovered. We propose new efficient methods to train these models without having to sample unobserved pairs.

Jon Tamir – Home

The onramp dynamics is modeled using an ordinary differential equation describing the evolution of the queue length. I am a member of the Laser group at Google Researchwhere I work on machine learning and recommendation. We illustrate these results on numerical examples. I like working with undergraduates on interesting projects. Berkeley EE Fall We also develop an adaptive averaging heuristic that empirically speeds up the convergence, and in many cases performs significantly better than popular heuristics such as restarting.

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Bart Workshop Materials Logo credit: After discretizing the corresponding partial differential equation models via the Godunov scheme, we detail the computation of the gradient of the discretized system with respect to the control parameters and show that the complexity of its computation scales linearly with the dissertarion of discrete state variables for networks of small vertex degree.

berkeley eecs dissertation talk

In particular, we find that there may exist multiple Nash equilibria that have different total costs. In particular, we give an averaging interpretation of accelerated dynamics, and derive simple sufficient conditions on the averaging scheme to guarantee a given rate of convergence. Archive Mentoring visiting research students Chedly Bourguiba Behavioral modeling using online learning. Benjamin received the Grand Prix d’option of Ecole Polytechnique.

We pose dissertqtion following estimation problem: The echo train ordering is randomly shuffled during the acquisition according to variable density Poisson disk sampling masks. Numerical simulations on the I15 freeway in California demonstrate an improvement in performance and running time compared with existing methods.

Kate Harrison

By studying the spectrum of the linearized system around rest points, we show that Nash equilibria are locally asymptotically stable stationary points. Online learning and convex optimization algorithms have become essential tools for solving problems in modern machine learning, statistics and engineering. The method mitigates image blur and rerospectively synthesizes T1-weighted and T2-weighted volumetric images.

In Summer I interned at Arterys. However, rather than seeing this as a problem, I believe scale can help classes. In this paper, we consider privacy in the routing game, where the origins and destinations of drivers are considered private. We make a connection between the discrete Hedge algorithm for online learning, and an ODE on the simplex, known as the replicator dynamics. And then, I present my deployment of a scaled hint intervention using the insights from the analysis.

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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. A simple Stackelberg strategy, ecs non-compliant first NCF strategy, is introduced, which can be computed in polynomial time, and it is shown to be optimal for this new class of latency on parallel networks.

Some time before that I interned at National Instruments. The method accounts for temporal dynamics during the echo trains to reduce image blur and resolve multiple image contrasts along the T2 relaxation curve. Delle Monache and J. In this thesis, we apply this paradigm to two problems: 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.

berkeley eecs dissertation talk

We consider a model in which players use regret-minimizing algorithms as the learning mechanism, and study the resulting dynamics. 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. The numerical approximation is carried out using a Godunov scheme, modified to take into account the effects of the onramp buffer.

We then show dsisertation, under beroeley assumptions, Dual Averaging on the infinite-dimensional space of probability distributions indeed achieves Hannan-consistency.

berkeley eecs dissertation talk

Adjoint-based optimization on a network of discretized scalar conservation law PDEs with applications to coordinated ramp metering. We develop a method to design an ODE for the problem using an inverse Lyapunov argument: