Optimization
Fast Proxy Experiment Design for Causal Effect Identification
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies.
Incentivizing Quality Text Generation via Statistical Contracts
We show our framework's flexibility by providing a comprehensive
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, Lisa Amini
Zeroth-order (gradient-free) optimization is increasingly embraced for solving machine learning problems where explicit expressions of the gradients are difficult or infeasible to obtain.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
In practice, most combinatorial optimization problems can be formulated as mixed-integer linear programs (MILPs), in which case branch-and-bound (B&B) [35] is the exact method of choice.
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
Cedric Josz, Yi Ouyang, Richard Zhang, Javad Lavaei, Somayeh Sojoudi
We study the set of continuous functions that admit no spurious local optima (i.e.
Global Identifiability of ℓ 1-based Dictionary Learning via Matrix Volume Optimization
On the theory side, most of the existing works have focused on algorithm design.
Global Identifiability of ℓ 1-based Dictionary Learning via Matrix Volume Optimization
On the theory side, most of the existing works have focused on algorithm design.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication Martijn de V os
We discuss these works in more detail in Section 5. Authors are listed in alphabetical order.