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Found 15 papers, 3 papers with code
Learning the Valuations of a $k$-demand Agent
We study problems where a learner aims to learn the valuations of an agent by observing which goods he buys under varying price vectors.
Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
We derive regret bounds in both the realizable setting where all agents manipulate according to the same graph within the graph family, and the agnostic setting where the manipulation graphs are chosen adversarially and not consistently modeled by a single graph in the family.
Efficient Algorithms for Planning with Participation Constraints
Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and $\log(1/\varepsilon)$, and returns a policy that is optimal up to an additive error of $\varepsilon$.
Prior-independent Dynamic Auctions for a Value-maximizing Buyer
We study prior-independent dynamic auction design with production costs for a value-maximizing buyer, a paradigm that is becoming prevalent recently following the development of automatic bidding algorithms in advertising platforms.
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.
Incentive-aware PAC learning
We give a sample complexity bound that is, curiously, independent of the hypothesis class, for the ERM principle restricted to incentivecompatible classifiers.
Automated Mechanism Design for Classification with Partial Verification
We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal's limited verification power.
Classification with Strategically Withheld Data
To this end, we present {\sc IC-LR}, a modification of Logistic Regression that removes the incentive to strategically drop features.
Planning with Submodular Objective Functions
We study planning with submodular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a submodular function.
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.
Nearly Linear Row Sampling Algorithm for Quantile Regression
We give a row sampling algorithm for the quantile loss function with sample complexity nearly linear in the dimensionality of the data, improving upon the previous best algorithm whose sampling complexity has at least cubic dependence on the dimensionality.
Distinguishing Distributions When Samples Are Strategically Transformed
In other settings, the principal may not even be able to observe samples directly; instead, she must rely on signals that the agent is able to send based on the samples that he obtains, and he will choose these signals strategically.
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i. e, approximating Q-functions with linear functions, it is still an open problem how to design a provably efficient algorithm that learns a near-optimal policy.
Provably Efficient $Q$-learning with Function Approximation via Distribution Shift Error Checking Oracle
Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i. e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a provably efficient algorithm that learns a near-optimal policy.
