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Found 8 papers, 0 papers with code
Obtaining Explainable Classification Models using Distributionally Robust Optimization
Model explainability is crucial for human users to be able to interpret how a proposed classifier assigns labels to data based on its feature values.
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
For example, when the total number of features in the source task's learning model is fixed, we show that it is more advantageous to allocate a greater number of redundant features to the task-specific part rather than the common part.
Topological data analysis on noisy quantum computers
In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
In contrast, our study first illustrates the benefits of incorporating a natural geometric structure within a linear regression model, which corresponds to the generalized eigenvalue problem formed by the Gram matrices of both domains.
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
In this paper, we completely overhaul the QTDA algorithm to achieve an improved exponential speedup and depth complexity of $O(n\log(1/(\delta\epsilon)))$.
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities.
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research.
A General Family of Robust Stochastic Operators for Reinforcement Learning
We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors.
