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Found 11 papers, 1 papers with code
Optimal Regret with Limited Adaptivity for Generalized Linear Contextual Bandits
For our batch learning algorithm B-GLinCB, with $\Omega\left( \log{\log T} \right)$ batches, the regret scales as $\tilde{O}(\sqrt{T})$.
Learning Good Interventions in Causal Graphs via Covering
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set $A$ of (possibly non-atomic) interventions over a given causal graph.
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Since NSW is known to satisfy fairness axioms, our approach complements the utilitarian considerations of average (cumulative) regret, wherein the algorithm is evaluated via the arithmetic mean of its expected rewards.
Intervention Efficient Algorithm for Two-Stage Causal MDPs
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards.
Optimal Algorithms for Range Searching over Multi-Armed Bandits
The sample complexities of our algorithms depend, in particular, on the size of the optimal hitting set of the given intervals.
Existence and Computation of Maximin Fair Allocations Under Matroid-Rank Valuations
We study fair and economically efficient allocation of indivisible goods among agents whose valuations are rank functions of matroids.
Computer Science and Game Theory
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
To quantify this bias, we propose a general notion of $\eta$-infra-marginality that can be used to evaluate the extent of this bias.
Fair Division Under Cardinality Constraints
In this setting, we are given a partition of the entire set of goods---i. e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent.
Groupwise Maximin Fair Allocation of Indivisible Goods
We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations.
Online Learning for Structured Loss Spaces
We consider prediction with expert advice when the loss vectors are assumed to lie in a set described by the sum of atomic norm balls.
Online Convex Optimization Using Predictions
Making use of predictions is a crucial, but under-explored, area of online algorithms.
