Avhishek Chatterjee

Work capacity of freelance markets: Fundamental limits and decentralized schemes

Crowdsourcing of jobs to online freelance markets is rapidly gaining popularity. Most crowdsourcing platforms are uncontrolled and offer freedom to customers and freelancers to choose each other. This works well for unskilled jobs (e.g., image classification) with no specific quality requirement since freelancers are functionally identical. For skilled jobs (e.g., software development) with specific requirements, however, this does not ensure the maximum number of job requests is satisfied. In this work we determine the capacity of freelance markets, in terms of maximum satisfied job requests, and propose centralized schemes that achieve capacity. To ensure decentralized operation and freedom of choice for customers and freelancers, we propose simple schemes compatible with the operation of current crowd-sourcing platforms that approximately achieve capacity. Further, for settings where job requests exceed capacity, we propose an optimal and fair scheme for declining jobs without wait.

Low Delay MAC Scheduling for Frequency-Agile Multi-Radio Wireless Networks

Recent trends suggest that cognitive radio based wireless networks will be frequency agile and the nodes will be equipped with multiple radios capable of tuning across large swaths of spectrum. The MAC scheduling problem in such networks refers to making intelligent decisions on which communication links to activate at which time instant and over which frequency band. The challenge in designing a low-complexity distributed MAC, that achieves low delay, is posed by two additional dimensions of cognitive radio networks: interference graphs and data rates that are frequency-band dependent, and explosion in number of feasible schedules due to large number of available frequency-bands. In this paper, we propose MAXIMAL-GAIN MAC, a distributed MAC scheduler for frequency agile multi-band networks that simultaneously achieves the following: (i) optimal network-delay scaling with respect to the number of communicating pairs, (ii) low computational complexity of O(log2(maximum degree of the interference graphs)) which is independent of the number of frequency bands, number of radios per node, and overall size of the network, and (iii) robustness, i.e., it can be adapted to a scenario where nodes are not synchronized and control packets could be lost. Our proposed MAC also achieves a throughput provably within a constant fraction (under isotropic propagation) of the maximum throughput. Due to a recent impossibility result, optimal delay-scaling could only be achieved with some amount of throughput loss . Extensive simulations using OMNeT++ network simulator shows that, compared to a multi-band extension of a state-of-art CSMA algorithm (namely, Q-CSMA), our asynchronous algorithm achieves a 2.5x reduction in delay while achieving at least 85% of the maximum achievable throughput. Our MAC algorithms are derived from a novel local search based technique.

Efficient and flexible crowdsourcing of specialized tasks with precedence constraints

Many companies now use crowdsourcing to leverage external (as well as internal) crowds to perform specialized work, and so methods of improving efficiency are critical. Tasks in crowdsourcing systems with specialized work have multiple steps and each step requires multiple skills. Steps may have different flexibilities in terms of obtaining service from one or multiple agents, due to varying levels of dependency among parts of steps. Steps of a task may have precedence constraints among them. Moreover, there are variations in loads of different types of tasks requiring different skill-sets and availabilities of different types of agents with different skill-sets. Considering these constraints together necessitates the design of novel schemes to allocate steps to agents. In addition, large crowdsourcing systems require allocation schemes that are simple, fast, decentralized and offer customers (task requesters) the freedom to choose agents. In this work we study the performance limits of such crowdsourcing systems and propose efficient allocation schemes that provably meet the performance limits under these additional requirements. We demonstrate our algorithms on data from a crowdsourcing platform run by a non-profit company and show significant improvements over current practice.

Epidemic thresholds with external agents

We study the effect of external infection sources on phase transitions in epidemic processes. In particular, we consider an epidemic spreading on a network via the SIS/SIR dynamics, which in addition is aided by external agents - sources unconstrained by the graph, but possessing a limited infection rate or virulence. Such a model captures many existing models of externally aided epidemics, and finds use in many settings - epidemiology, marketing and advertising, network robustness, etc. We provide a detailed characterization of the impact of external agents on epidemic thresholds. In particular, for the SIS model, we show that any external infection strategy with constant virulence either fails to significantly affect the lifetime of an epidemic, or at best, sustains the epidemic for a lifetime which is polynomial in the number of nodes. On the other hand, a random external-infection strategy, with rate increasing linearly in the number of infected nodes, succeeds under some conditions to sustain an exponential epidemic lifetime. We obtain similar sharp thresholds for the SIR model, and discuss the relevance of our results in a variety of settings.

Stochastic bounded confidence opinion dynamics

In a vast body of opinion dynamics literature, an agent updates its opinion based on the opinions of its neighbors in a static social graph, regardless of their differences in opinions. In contrast, the bounded confidence opinion dynamics does not presume a static interaction graph, and instead limits interactions to those agents that share related opinions (i.e., whose opinions are close to one another). We generalize the bounded confidence opinion dynamics model by incorporating stochastic interactions based on opinion differences and the endogenous evolution of the agent opinions, which itself is a random process. We analytically characterize the conditions under which this stochastic dynamics is stable in an appropriate sense.

Pairwise stochastic bounded confidence opinion dynamics: Heavy tails and stability

Traditional models in opinion dynamics involve agents updating their opinions based on the opinions of their neighbors in a static social-graph, regardless of their differences in opinions. In contrast, the bounded confidence opinion dynamics does not presume a static interaction graph, and instead models interactions between those agents that share similar opinions (i.e., are close to one another, capturing online discussion groups and conventional meetings). We generalize the bounded confidence opinion dynamics model by incorporating pairwise stochastic interactions based on opinion differences as well as the self or endogenous evolution of the agent opinions, which is represented by a random process. We analytically characterize the conditions under which this stochastic dynamics is stable in an appropriate sense. This characterization relates well to what is observed in social systems. Moreover, this generalization sheds light on dynamics that combine aspects of graph-based updates and bounded confidence models.

Learning the causal graph of Markov time series

This paper considers a natural and widely prevalent setting where a collection of one dimensional time series evolve in a causal manner, and one is interested in inferring the graph governing the causality between these processes in a high dimensional setting. We consider this problem in the special case where variables are discrete and updates are Markov. We develop a new algorithm to learn causal graph structure based on the notion of directed information, and analytically and empirically demonstrate its performance. Our algorithm is an adaptation of a greedy heuristic for learning undirected graphical models, with modifications to leverage causality. Analytically, the challenge lies in determining sample complexity, given the dependencies between samples.

Towards optimal quantization of neural networks

Due to the unprecedented success of deep neural networks in inference tasks like speech and image recognition, there has been increasing interest in using them in mobile and in-sensor applications. As most current deep neural networks are very large in size, a major challenge lies in storing the network in devices with limited memory. Consequently there is growing interest in compressing deep networks by quantizing synaptic weights, but most prior work is heuristic and lacking theoretical foundations. Here we develop an approach to quantizing deep networks using functional high-rate quantization theory. Under certain technical conditions, this approach leads to an optimal quantizer that is computed using the celebrated backpropagation algorithm. In all other cases, a heuristic quantizer with certain regularization guarantees can be computed.

Energy-reliability limits in nanoscale neural networks

New device technologies such as spintronics, carbon nanotubes, and nanoscale CMOS incur random transient failures, where the failure probability is governed by the energy consumption through energy-failure functions. At the same time, there is growing use of deep neural networks for many inference applications, and specialized hardware is being developed with these nanotechnologies as physical substrates. It is important to understand the basic energy-reliability limits. Using Pippengers mutual information propagation technique (extended to directed acyclic graphs), together with optimization, we obtain a lower bound on energy consumption in multilayer binary neural networks for a given reliability. We also obtain a simple energy allocation rule for neurons in the different layers of the neural network. The mathematical results also provide insight into mammalian neuroenergetics of brain regions involved in sensory processing.

Capacity of Systems with Queue-Length Dependent Service Quality

We study the information-theoretic limit of reliable information processing by a server with queue-length dependent quality of service. We define the capacity for such a system as the number of bits reliably processed per unit time, and characterize it in terms of queuing system parameters. We also characterize the distributions of the arrival and service processes that maximize and minimize the capacity of such systems, observing a minimum around the memoryless distribution. The problem is theoretically motivated by an effort to incorporate the notion of reliability in queueing systems, and is applicable in contexts of multimedia communication, crowdsourcing, and stream computing.