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Found 7 papers, 1 papers with code
Dynamic Algorithms for Matroid Submodular Maximization
This is the first dynamic algorithm for the problem that has a query complexity independent of the size of ground set $V$.
Optimal Sparse Recovery with Decision Stumps
Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood.
Bandit Social Learning: Exploration under Myopic Behavior
We study social learning dynamics motivated by reviews on online platforms.
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks
Based on this approach, we propose DPA+ROE and FA+ROE defense methods based on Deep Partition Aggregation (DPA) and Finite Aggregation (FA) approaches from prior work.
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
In this setting, through theoretical and empirical analysis, we show that (i) adversarial training with $\ell_1$ and $\ell_2$ norms increases the model reliance on spurious features; (ii) For $\ell_\infty$ adversarial training, spurious reliance only occurs when the scale of the spurious features is larger than that of the core features; (iii) adversarial training can have an unintended consequence in reducing distributional robustness, specifically when spurious correlations are changed in the new test domain.
Admissible Policy Teaching through Reward Design
This problem can be viewed as a dual to the problem of optimal reward poisoning attacks: instead of forcing an agent to adopt a specific policy, the reward designer incentivizes an agent to avoid taking actions that are inadmissible in certain states.
Defense Against Reward Poisoning Attacks in Reinforcement Learning
As a threat model, we consider attacks that minimally alter rewards to make the attacker's target policy uniquely optimal under the poisoned rewards, with the optimality gap specified by an attack parameter.
