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Found 22 papers, 3 papers with code
Online Stackelberg Optimization via Nonlinear Control
In repeated interaction problems with adaptive agents, our objective often requires anticipating and optimizing over the space of possible agent responses.
Shill-Proof Auctions
In an auction, a seller may masquerade as one or more bidders in order to manipulate the clearing price.
Utilitarian Algorithm Configuration
We prove upper bounds on the runtime of these procedures that are similar to theoretical lower bounds, while also demonstrating their performance empirically.
Automated Market Making and Arbitrage Profits in the Presence of Fees
We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage.
Automated Market Making and Loss-Versus-Rebalancing
We consider the market microstructure of automated market makers (AMMs) from the perspective of liquidity providers (LPs).
Formalizing Preferences Over Runtime Distributions
We propose a principled alternative, taking a utility-theoretic approach to characterize the scoring functions that describe preferences over algorithms.
Are You Smarter Than a Random Expert? The Robust Aggregation of Substitutable Signals
We show that by averaging the experts' forecasts and then \emph{extremizing} the average by moving it away from the prior by a constant factor, the aggregator's performance guarantee is substantially better than is possible without knowledge of the prior.
Smoothed Analysis with Adaptive Adversaries
-Online discrepancy minimization: We consider the online Koml\'os problem, where the input is generated from an adaptive sequence of $\sigma$-smooth and isotropic distributions on the $\ell_2$ unit ball.
Byzantine Generals in the Permissionless Setting
What differentiates Bitcoin from these previously studied protocols is that it operates in a permissionless setting, i. e. it is a protocol for establishing consensus over an unknown network of participants that anybody can join, with as many identities as they like in any role.
Distributed Computing
Distributed, Parallel, and Cluster Computing
Beyond the Worst-Case Analysis of Algorithms (Introduction)
One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem.
Smoothed Analysis of Online and Differentially Private Learning
Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms.
On the Computational Power of Online Gradient Descent
We prove that the evolution of weight vectors in online gradient descent can encode arbitrary polynomial-space computations, even in very simple learning settings.
An Optimal Algorithm for Online Unconstrained Submodular Maximization
The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight.
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Our main result is the first $\frac{1}{2}$-approximation algorithm for continuous submodular function maximization; this approximation factor of $\frac{1}{2}$ is the best possible for algorithms that only query the objective function at polynomially many points.
The idemetric property: when most distances are (almost) the same
We introduce the \emph{idemetric} property, which formalises the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard amongst small-world network models.
Social and Information Networks Discrete Mathematics
The Price of Anarchy in Auctions
This survey outlines a general and modular theory for proving approximation guarantees for equilibria of auctions in complex settings.
Learning Simple Auctions
We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of "simple" auctions.
On the Pseudo-Dimension of Nearly Optimal Auctions
This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data.
A PAC Approach to Application-Specific Algorithm Selection
While there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem.
Tight Error Bounds for Structured Prediction
We show that the prospects for achieving low expected Hamming error depend on the structure of the graph $G$ in interesting ways.
Privately Solving Linear Programs
In this paper, we initiate the systematic study of solving linear programs under differential privacy.
Marginals-to-Models Reducibility
We consider a number of classical and new computational problems regarding marginal distributions, and inference in models specifying a full joint distribution.
