BPRS: Belief Propagation Based Iterative Recommender System

In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings to be predicted. However, computing these marginal probability functions is computationally prohibitive for large-scale systems. Therefore, we utilize the BP algorithm to efficiently compute these functions. Recommendations for each active user are then iteratively computed by probabilistic message passing. As opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active. Further, BPRS computes the recommendations for each user with linear complexity and without requiring a training period. Via computer simulations (using the 100K MovieLens dataset), we verify that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Finally, we confirm that BPRS is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Therefore, we believe that the BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems

Sensing and molecular communication using synthetic cells: Theory and algorithms

Molecular communication (MC) is a novel communication paradigm in which molecules are used to encode, transmit and decode information. MC is the primary method by which biological entities exchange information and hence, cooperate with each other. MC is a promising paradigm to enable communication between nano-bio machines, e.g., biosensors with potential applications such as cancer and disease detection, smart drug delivery, toxicity detection etc. The objective of this research is to establish the fundamentals of diffusion-based molecular communication and sensing via biological agents (e.g., synthetic bacteria) from a communication and information theory perspective, and design algorithms for reliable communication and sensing systems. In the first part of the thesis, we develop models for the diffusion channel as well as the molecular sensing at the receiver and obtain the maximum achievable rate for such a communication system. Next, we study reliability in MC. We design practical nodes by employing synthetic bacteria as the basic element of a biologically-compatible communication system and show how reliable nodes can be formed out of the collective behavior of a population of unreliable bio-agents. We model the probabilistic behavior of bacteria, obtain the node sensing capacity and propose a practical modulation scheme. In order to improve the reliability, we also introduce relaying and error-detecting codes for MC. In the second part of the thesis, we study the molecular sensing problem with potential applications in disease detection. We establish the rate-distortion theory for molecular sensing and investigate as to how distortion can be minimized via an optimal quantizer. We also study sensor cell arrays in which sensing redundancy is achieved by using multiple sensors to measure several molecular inputs simultaneously. We study the interference in sensing molecular inputs and propose a probabilistic message passing algorithm to solve the pattern detection over the molecular inputs of interest.Ph.D

Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data. Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.Comment: Accepted for AAAI-2020 Main Trac