68 citations · 185 across the 18 of their papers we have counts for
21 papers
T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendation
Yang Liu, Noel Loo, Ali Khanafer +4
Large-scale recommenders increasingly adopt the sequential generative recipe behind large language models, bringing the Transformer into recommendation along with design choices ma…
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
Kunal Dahiya, Nilesh Gupta, Deepak Saini +16
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representation…
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Kunal Dahiya, Deepak Saini, Anshul Mittal +6
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point…
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Junhan Yang, Zheng Liu, Bowen Jin +7
Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates t…
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
Jianxun Lian, Iyad Batal, Zheng Liu +4
Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particul…
Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising
Iyad Batal, Akshay Soni
Multiple content providers rely on native advertisement for revenue by placing ads within the organic content of their pages. We refer to this setting as ``queryless'' to different…
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
Bowen Weng, Huaqing Xiong, Yingbin Liang +1
Existing convergence analyses of Q-learning mostly focus on the vanilla stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment Estimation (Adam) has been co…
Towards Automated Single Channel Source Separation using Neural Networks
Arpita Gang, Pravesh Biyani, Akshay Soni
Many applications of single channel source separation (SCSS) including automatic speech recognition (ASR), hearing aids etc. require an estimation of only one source from a mixture…
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh V. Nguyen, Akshay Soni, Chinmay Hegde
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such a…
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Sheng Chen, Akshay Soni, Aasish Pappu +1
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit sev…
Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
Jeya Balaji Balasubramanian, Akshay Soni, Yashar Mehdad +1
The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time. However, like in most real-world applicati…
Rank-to-engage: New Listwise Approaches to Maximize Engagement
Swayambhoo Jain, Akshay Soni, Nikolay Laptev +1
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is c…
Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping
Parikshit Shah, Akshay Soni, Troy Chevalier
We study the online constrained ranking problem motivated by an application to web-traffic shaping: an online stream of sessions arrive in which, within each session, we are asked…
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
Akshay Soni, Yashar Mehdad
The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that onl…
Noisy Inductive Matrix Completion Under Sparse Factor Models
Akshay Soni, Troy Chevalier, Swayambhoo Jain
Inductive Matrix Completion (IMC) is an important class of matrix completion problems that allows direct inclusion of available features to enhance estimation capabilities. These m…
Distributed Representations for Biological Sequence Analysis
Dhananjay Kimothi, Akshay Soni, Pravesh Biyani +1
Biological sequence comparison is a key step in inferring the relatedness of various organisms and the functional similarity of their components. Thanks to the Next Generation Sequ…
Noisy Matrix Completion under Sparse Factor Models
Akshay Soni, Swayambhoo Jain, Jarvis Haupt +1
This paper examines a general class of noisy matrix completion tasks where the goal is to estimate a matrix from observations obtained at a subset of its entries, each of which is…
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
Swayambhoo Jain, Akshay Soni, Jarvis Haupt
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-meas…
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Akshay Soni, Jarvis Haupt
Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adapti…
Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
Akshay Soni, Jarvis Haupt
Estimating the level set of a signal from measurements is a task that arises in a variety of fields, including medical imaging, astronomy, and digital elevation mapping. Motivated…
Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned Dictionaries
Akshay Soni, Jarvis Haupt
Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non- adapti…