Robert Kleinberg
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View article: The Keychain Problem: On Minimizing the Opportunity Cost of Uncertainty
The Keychain Problem: On Minimizing the Opportunity Cost of Uncertainty Open
In this paper, we introduce a family of sequential decision-making problems, collectively called the Keychain Problem, that involve exploring a set of actions to maximize expected payoff when only a subset of actions are available in each …
View article: Full Swap Regret and Discretized Calibration
Full Swap Regret and Discretized Calibration Open
We study the problem of minimizing swap regret in structured normal-form games. Players have a very large (potentially infinite) number of pure actions, but each action has an embedding into $d$-dimensional space and payoffs are given by b…
View article: Near-Optimal Algorithms for Omniprediction
Near-Optimal Algorithms for Omniprediction Open
Omnipredictors are simple prediction functions that encode loss-minimizing predictions with respect to a hypothesis class $H$, simultaneously for every loss function within a class of losses $L$. In this work, we give near-optimal learning…
View article: Distributed Load Balancing with Workload-Dependent Service Rates
Distributed Load Balancing with Workload-Dependent Service Rates Open
We study distributed load balancing in bipartite queueing systems where frontends route jobs to heterogeneous backends with workload-dependent service rates. The system's connectivity -- governed by compatibility constraints such as data r…
View article: Online Matroid Embeddings
Online Matroid Embeddings Open
We introduce the notion of an online matroid embedding, which is an algorithm for mapping an unknown matroid that is revealed in an online fashion to a larger-but-known matroid. We establish the existence of such an embedding for binary ma…
View article: Breaking the $T^{2/3}$ Barrier for Sequential Calibration
Breaking the $T^{2/3}$ Barrier for Sequential Calibration Open
A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where that prediction was made. We study the fundamental problem of …
View article: Breaking the VLB Barrier for Oblivious Reconfigurable Networks
Breaking the VLB Barrier for Oblivious Reconfigurable Networks Open
In a landmark 1981 paper, Valiant and Brebner gave birth to the study of oblivious routing and, simultaneously, introduced its most powerful and ubiquitous method: Valiant load balancing (VLB). By routing messages through a randomly sample…
View article: Load is not what you should balance: Introducing Prequal
Load is not what you should balance: Introducing Prequal Open
We present Prequal (Probing to Reduce Queuing and Latency), a load balancer for distributed multi-tenant systems. Prequal aims to minimize real-time request latency in the presence of heterogeneous server capacities and non-uniform, time-v…
View article: Faster Recalibration of an Online Predictor via Approachability
Faster Recalibration of an Online Predictor via Approachability Open
Predictive models in ML need to be trustworthy and reliable, which often at the very least means outputting calibrated probabilities. This can be particularly difficult to guarantee in the online prediction setting when the outcome sequenc…
View article: Breaking the VLB Barrier for Oblivious Reconfigurable Networks
Breaking the VLB Barrier for Oblivious Reconfigurable Networks Open
In a landmark 1981 paper, Valiant and Brebner gave birth to the study of oblivious routing and, simultaneously, introduced its most powerful and ubiquitous method: Valiant load balancing (VLB). By routing messages through a randomly sample…
View article: U-Calibration: Forecasting for an Unknown Agent
U-Calibration: Forecasting for an Unknown Agent Open
We consider the problem of evaluating forecasts of binary events whose predictions are consumed by rational agents who take an action in response to a prediction, but whose utility is unknown to the forecaster. We show that optimizing fore…
View article: Non-Stochastic CDF Estimation Using Threshold Queries
Non-Stochastic CDF Estimation Using Threshold Queries Open
Estimating the empirical distribution of a scalar-valued data set is a basic and fundamental task. In this paper, we tackle the problem of estimating an empirical distribution in a setting with two challenging features. First, the algorith…
View article: Online Convex Optimization with Unbounded Memory
Online Convex Optimization with Unbounded Memory Open
Online convex optimization (OCO) is a widely used framework in online learning. In each round, the learner chooses a decision in a convex set and an adversary chooses a convex loss function, and then the learner suffers the loss associated…
View article: Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem Open
Bandits with knapsacks (BwK) is an influential model of sequential decision-making under uncertainty that incorporates resource consumption constraints. In each round, the decision-maker observes an outcome consisting of a reward and a vec…
View article: Individual Fairness in Prophet Inequalities
Individual Fairness in Prophet Inequalities Open
Prophet inequalities are performance guarantees for online algorithms (a.k.a. stopping rules) solving the following "hiring problem": a decision maker sequentially inspects candidates whose values are independent random numbers and is aske…
View article: Optimal Oblivious Reconfigurable Networks
Optimal Oblivious Reconfigurable Networks Open
Oblivious routing has a long history in both the theory and practice of networking. In this work we initiate the formal study of oblivious routing in the context of reconfigurable networks, a new architecture that has recently come to the …
View article: Threshold Tests as Quality Signals: Optimal Strategies, Equilibria, and\n Price of Anarchy
Threshold Tests as Quality Signals: Optimal Strategies, Equilibria, and\n Price of Anarchy Open
We study a signaling game between two firms competing to have their product\nchosen by a principal. The products have qualities drawn i.i.d. from a common\nprior. The principal aims to choose the better product, but the quality of a\nprodu…
View article: Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem
Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem Open
We present the first nontrivial approximation algorithm for the bottleneck asymmetric traveling salesman problem . Given an asymmetric metric cost between n vertices, the problem is to find a Hamiltonian cycle that minimizes its bottleneck…
View article: A Measure of Polarization on Social Media Networks Based on Community Boundaries
A Measure of Polarization on Social Media Networks Based on Community Boundaries Open
Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a long-term challenge to r…
View article: Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points
Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points Open
One of the central human biases studied in behavioral economics is reference dependence - people's tendency to evaluate an outcome not in absolute terms but instead relative to a reference point that reflects some notion of the status quo …
View article: Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points
Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points Open
People are often reluctant to sell a house, or shares of stock, below the price at which they originally bought it. While this is generally not consistent with rational utility maximization, it does reflect two strong empirical regularitie…
View article: Bernoulli Factories and Black-box Reductions in Mechanism Design
Bernoulli Factories and Black-box Reductions in Mechanism Design Open
We provide a polynomial time reduction from Bayesian incentive compatible mechanism design to Bayesian algorithm design for welfare maximization problems. Unlike prior results, our reduction achieves exact incentive compatibility for probl…
View article: Constrained-Order Prophet Inequalities
Constrained-Order Prophet Inequalities Open
Free order prophet inequalities bound the ratio between the expected value obtained by two parties each selecting a value from a set of independent random variables: a "prophet" who knows the value of each variable and may select the maxim…
View article: Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem
Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem Open
We present the first nontrivial approximation algorithm for the bottleneck asymmetric traveling salesman problem. Given an asymmetric metric cost between n vertices, the problem is to find a Hamiltonian cycle that minimizes its bottleneck …