Denis X. Charles

Signatures for Network Coding

This paper presents a practical digital signature scheme to be used in conjunction with network coding. Our scheme simultaneously provides authentication and detects malicious nodes that intentionally corrupt content on the network.

Signatures for network coding

This paper presents a practical digital signature scheme to be used in conjunction with network coding. Our scheme simultaneously provides authentication and detects malicious nodes that intentionally corrupt content on the network.

Structured labeling for facilitating concept evolution in machine learning

Labeling data is a seemingly simple task required for training many machine learning systems, but is actually fraught with problems. This paper introduces the notion of concept evolution, the changing nature of a persons underlying concept (the abstract notion of the target class a person is labeling for, e.g., spam email, travel related web pages) which can result in inconsistent labels and thus be detrimental to machine learning. We introduce two structured labeling solutions, a novel technique we propose for helping people define and refine their concept in a consistent manner as they label. Through a series of five experiments, including a controlled lab study, we illustrate the impact and dynamics of concept evolution in practice and show that structured labeling helps people label more consistently in the presence of concept evolution than traditional labeling.

Bloomier Filters: A Second Look

A Bloom filter is a space efficient structure for storing static sets, where the space efficiency is gained at the expense of a small probability of false-positives. A Bloomier filtergeneralizes a Bloom filter to compactly store a function with a static support. In this article we give a simple construction of a Bloomier filter. The construction is linear in space and requires constant time to evaluate. The creation of our Bloomier filter takes linear time which is faster than the existing construction. We show how one can improve the space utilization further at the cost of increasing the time for creating the data structure.

Fast algorithms for finding matchings in lopsided bipartite graphs with applications to display ads

We derive efficient algorithms for both detecting and representing matchings in lopsided bipartite graphs; such graphs have so many nodes on one side that it is infeasible to represent them in memory or to identify matchings using standard approaches. Detecting and representing matchings in lopsided bipartite graphs is important for allocating and delivering guaranteed-placement display ads, where the corresponding bipartite graph of interest has nodes representing advertisers on one side and nodes representing web-page impressions on the other; real-world instances of such graphs can have billions of impression nodes. We provide theoretical guarantees for our algorithms, and in a real-world advertising application, we demonstrate the feasibility of our detection algorithms.

Computing Modular Polynomials

This paper presents a new probabilistic algorithm to compute modular polynomials modulo a prime. Modular polynomials parameterize pairs of isogenous elliptic curves, and are useful in many aspects of computational number theory and cryptography. The algorithm presented here has the distinguishing feature that it does not involve the computation of Fourier coefficients of modular forms. The need to compute the exponentially large integral coefficients is avoided by working directly modulo a prime, and computing isogenies between elliptic curves via Velus formulas.

Market design & analysis for a P2P backup system

In this paper we take the problem of a market-based P2P backup application and carry it through market design, to implementation, to theoretical and experimental analysis. While the long-term goal is an open market using real money, here we consider a system where monetary transfers are prohibited. We first describe the design of the P2P resource exchange market and the UI we developed. Second, we prove theorems on equilibrium existence and uniqueness. Third, we prove a surprising impossibility result regarding the limited controllability of the equilibrium and show how to address this. Fourth, we present a price update algorithm that uses daily supply and demand information to move prices towards the equilibrium and we provide a theoretical and experimental convergence analysis. The market design described in this paper is already implemented as part of a Microsoft research project on P2P backup systems and an alpha version of the software has been successfully tested.

Budget smoothing for internet ad auctions: a game theoretic approach

In Internet ad auctions, search engines often throttle budget constrained advertisers so as to spread their spends across the specified time period. Such policies are known as budget smoothing policies. In this paper, we perform a principled, game-theoretic study of what the outcome of an ideal budget smoothing algorithm should be. In particular, we propose the notion of regret-free budget smoothing policies whose outcomes throttle each advertiser optimally, given the participation of the other advertisers. We show that regret-free budget smoothing policies always exist, and in the case of single slot auctions we can give a polynomial time smoothing algorithm. Inspired by the existence proof, we design a heuristic for budget smoothing which performs considerably better than existing benchmark heuristics.

GigaHash: scalable minimal perfect hashing for billions of urls

A minimal perfect function maps a static set of n keys on to the range of integers {0,1,2,...,n - 1}. We present a scalable high performance algorithm based on random graphs for constructing minimal perfect hash functions (MPHFs). For a set of n keys, our algorithm outputs a description of h in expected time O(n). The evaluation of h(x) requires three memory accesses for any key x and the description of h takes up 0.89n bytes (7.13n bits). This is the best (most space efficient) known result to date. Using a simple heuristic and Huffman coding, the space requirement is further reduced to 0.79n bytes (6.86n bits). We present a high performance architecture that is easy to parallelize and scales well to very large data sets encountered in internet search applications. Experimental results on a one billion URL dataset obtained from Live Search crawl data, show that the proposed algorithm (a)finds an MPHF for one billion URLs in less than 4 minutes, and (b) requires only 6.86 bits/key for the description of h.

Multi-Score Position Auctions

In this paper we propose a general family of position auctions used in paid search, which we call multi-score position auctions. These auctions contain the GSP auction and the GSP auction with squashing as special cases. We show experimentally that these auctions contain special cases that perform better than the GSP auction with squashing, in terms of revenue, and the number of clicks on ads. In particular, we study in detail the special case that squashes the first slot alone and show that this beats pure squashing (which squashes all slots uniformly). We study the equilibria that arise in this special case to examine both the first order and the second order effect of moving from the squashing-all-slots auction to the squash-only-the-top-slot auction. For studying the second order effect, we simulate auctions using the value-relevance correlated distribution suggested in Lahaie and Pennock [2007]. Since this distribution is derived from a study of value and relevance distributions in Yahoo! we believe the insights derived from this simulation to be valuable. For measuring the first order effect, in addition to the said simulation, we also conduct experiments using auction data from Bing over several weeks that includes a random sample of all auctions.