Search Results for author:
Found 12 papers, 7 papers with code
On the Iteration Complexity of Hypergradient Computations
We study a general class of bilevel optimization problems, in which the upper-level objective is defined via the solution of a fixed point equation.
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map.
Is Mamba Capable of In-Context Learning?
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model.
Learning invariant representations of time-homogeneous stochastic dynamical systems
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that faithfully captures its dynamics.
Group Meritocratic Fairness in Linear Contextual Bandits
This is a very strong notion of fairness, since the relative rank is not directly observed by the agent and depends on the underlying reward model and on the distribution of rewards.
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start
We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map.
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy.
Convergence Properties of Stochastic Hypergradients
Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning.
On the Iteration Complexity of Hypergradient Computation
We study a general class of bilevel problems, consisting in the minimization of an upper-level objective which depends on the solution to a parametric fixed-point equation.
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
We study the problem of learning-to-learn: inferring a learning algorithm that works well on tasks sampled from an unknown distribution.
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning.
