Tushar Deepak Chandra

Tushar Deepak Chandra

Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
    Ajit Apte
    Ambarish Jash
    Amol H Wankhede
    Ankit Kumar
    Ayooluwakunmi Jeje
    Dima Kuzmin
    Ellie Ka In Chio
    Harry Fung
    Jon Effrat
    Nitin Jindal
    Pei Cao
    Senqiang Zhou
    Sukhdeep S. Sodhi
    Tameen Khan
    Tarush Bali
    KDD(2021)
    Preview abstract As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts. View details
    Zero-Shot Transfer Learning for Query-Item Cold Start in Search Retrieval and Recommendations
    Ankit Kumar
    Cosmo Du
    Dima Kuzmin
    Ellie Chio
    John Roberts Anderson
    Li Zhang
    Nitin Jindal
    Pei Cao
    Ritesh Agarwal
    Steffen Rendle
    Tao Wu
    Wen Li
    CIKM(2020)
    Preview abstract Most search retrieval and recommender systems predict top-K items given a query by learning directly from a large training set of (query, item) pairs, where a query can include natural language (NL), user, and context features. These approaches fall into the traditional supervised learning framework where the algorithm trains on labeled data from the target task. In this paper, we propose a new zero-shot transfer learning framework, which first learns representations of items and their NL features by predicting (item, item) correlation graphs as an auxiliary task, followed by transferring learned representations to solve the target task (query-to-item prediction), without having seen any (query, item) pairs in training. The advantages of applying this new framework include: (1) Cold-starting search and recommenders without abundant query-item data; (2) Generalizing to previously unseen or rare (query, item) pairs and alleviating the "rich get richer" problem; (3) Transferring knowledge of (item, item) correlation from domains outside of search. We show that the framework is effective on a large-scale search and recommender system. View details
    Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
    Vihan Jain
    Jing Wang
    Sanmit Narvekar
    Ritesh Agarwal
    Rui Wu
    Morgane Lustman
    Vince Gatto
    Paul Covington
    Jim McFadden
    arXiv(2019)
    Preview abstract Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learning (RL) methods offer the potential to optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. In this work, we address the challenge of making slate-based recommendations to optimize long-term value using RL. Our contributions are three-fold. (i) We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user-choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. (ii) We outline a methodology that leverages existing myopic learning-based recommenders to quickly develop a recommender that handles LTV. (iii) We demonstrate our methods in simulation, and validate the scalability of decomposed TD-learning using SlateQ in live experiments on YouTube. View details
    SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
    Vihan Jain
    Jing Wang
    Sanmit Narvekar
    Ritesh Agarwal
    Rui Wu
    Proceedings of the Twenty-eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macau, China(2019), pp. 2592-2599
    Preview abstract Reinforcement learning (RL) methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. We demonstrate our methods in simulation, and validate the scalability and effectiveness of decomposed TD-learning on YouTube. View details
    Wide & Deep Learning for Recommender Systems
    Levent Koc
    Tal Shaked
    Glen Anderson
    Wei Chai
    Mustafa Ispir
    Rohan Anil
    Lichan Hong
    Vihan Jain
    Xiaobing Liu
    Hemal Shah
    arXiv:1606.07792(2016)
    Preview abstract Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. View details
    Efficient projections onto the <em>l</em><sub>1</sub>-ball for learning in high dimensions
    John Duchi
    Shai Shalev-Shwartz
    Yoram Singer
    ICML '08: Proceedings of the 25th international conference on Machine learning, ACM, New York, NY, USA(2008), pp. 272-279
    Preview
    Paxos Made Live - An Engineering Perspective (2006 Invited Talk)
    Joshua Redstone
    Proceedings of the 26th Annual ACM Symposium on Principles of Distributed Computing, ACM press(2007)
    Preview abstract We describe our experience in building a fault-tolerant data-base using the Paxos consensus algorithm. Despite the existing literature in the field, building such a database proved to be non-trivial. We describe selected algorithmic and engineering problems encountered, and the solutions we found for them. Our measurements indicate that we have built a competitive system. View details
    Bigtable: A Distributed Storage System for Structured Data
    Fay Chang
    Deborah A. Wallach
    Mike Burrows
    Andrew Fikes
    7th USENIX Symposium on Operating Systems Design and Implementation (OSDI), {USENIX}(2006), pp. 205-218
    Preview abstract Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable. View details
    Generalized Irreducibility of Consensus and the Equivalence of t-Resilient and Wait-Free Implementations of Consensus
    Vassos Hadzilacos
    Prasad Jayanti
    Sam Toueg
    SIAM J. Comput., 34(2004), pp. 333-357
    On scalable and efficient distributed failure detectors
    Indranil Gupta
    Germán S. Goldszmidt
    PODC(2001), pp. 170-179
    An Efficient Multicast Protocol for Content-Based Publish-Subscribe Systems
    Guruduth Banavar
    Bodhi Mukherjee
    Jay Nagarajarao
    Robert E. Strom
    Daniel C. Sturman
    ICDCS(1999), pp. 262-272
    Matching Events in a Content-Based Subscription System
    Marcos Kawazoe Aguilera
    Robert E. Strom
    Daniel C. Sturman
    Mark Astley
    PODC(1999), pp. 53-61
    The Cost of Graceful Degradation for Omission Failures
    Prasad Jayanti
    Sam Toueg
    Inf. Process. Lett., 71(1999), pp. 167-172
    A Case for Message Oriented Middleware
    Guruduth Banavar
    Robert E. Strom
    Daniel C. Sturman
    DISC(1999), pp. 1-18
    Gryphon: An Information Flow Based Approach to Message Brokering
    Robert E. Strom
    Guruduth Banavar
    Marc Kaplan
    Kevan Miller
    Bodhi Mukherjee
    Daniel C. Sturman
    Michael Ward
    CoRR, cs.DC/9810019(1998)
    Fault-Tolerant Wait-Free Shared Objects
    Prasad Jayanti
    Sam Toueg
    J. ACM, 45(1998), pp. 451-500
    A Polylog Time Wait-Free Construction for Closed Objects
    Prasad Jayanti
    King Tan
    PODC(1998), pp. 287-296
    The Weakest Failure Detector for Solving Consensus
    Vassos Hadzilacos
    Sam Toueg
    J. ACM, 43(1996), pp. 685-722
    Polylog Randomized Wait-Free Consensus
    PODC(1996), pp. 166-175
    Unreliable Failure Detectors for Reliable Distributed Systems
    Sam Toueg
    J. ACM, 43(1996), pp. 225-267
    On the Impossibility of Group Membership
    Vassos Hadzilacos
    Sam Toueg
    PODC(1996), pp. 322-330
    Wait-Freedom vs. t-Resiliency and the Robustness of Wait-Free Hierarchies
    Vassos Hadzilacos
    Prasad Jayanti
    Sam Toueg
    PODC(1994), pp. 334-343
    The Weakest Failure Detector for Solving Consensus
    Vassos Hadzilacos
    Sam Toueg
    PODC(1992), pp. 147-158
    Fault-tolerant Wait-free Shared Objects
    Prasad Jayanti
    Sam Toueg
    FOCS(1992), pp. 157-166
    Unreliable Failure Detectors for Asynchronous Systems (Preliminary Version)
    Sam Toueg
    PODC(1991), pp. 325-340
    Time and Message Efficient Reliable Broadcasts
    Sam Toueg
    WDAG(1990), pp. 289-303