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Found 10 papers, 5 papers with code
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
We develop a principled approach to end-to-end learning in stochastic optimization.
Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
We propose policy gradient algorithms for robust infinite-horizon Markov decision processes (MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the robust MDP literature.
Optimal Learning via Moderate Deviations Theory
This paper proposes a statistically optimal approach for learning a function value using a confidence interval in a wide range of models, including general non-parametric estimation of an expected loss described as a stochastic programming problem or various SDE models.
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
Counterexample-guided repair aims at creating neural networks with mathematical safety guarantees, facilitating the application of neural networks in safety-critical domains.
Distributionally Robust Optimization with Markovian Data
We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with $d$ states.
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Training models that perform well under distribution shifts is a central challenge in machine learning.
Topological Linear System Identification via Moderate Deviations Theory
Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space.
Optimization and Control
Generalized maximum entropy estimation
We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise.
A variational approach to path estimation and parameter inference of hidden diffusion processes
We consider a hidden Markov model, where the signal process, given by a diffusion, is only indirectly observed through some noisy measurements.
