25 citations · 87 across the 15 of their papers we have counts for
15 papers
Differentiable Adversarial Attacks for Marked Temporal Point Processes
Pritish Chakraborty, Vinayak Gupta, Rahul R +2
Marked temporal point processes (MTPPs) have been shown to be extremely effective in modeling continuous time event sequences (CTESs). In this work, we present adversarial attacks…
Are Language Models Actually Useful for Time Series Forecasting?
Mingtian Tan, Mike A. Merrill, Vinayak Gupta +2
Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent…
SPML: A DSL for Defending Language Models Against Prompt Attacks
Reshabh K Sharma, Vinayak Gupta, Dan Grossman
Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, po…
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows
Vinayak Gupta, Srikanta Bedathur
Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. Any human activity can be represented as a tempor…
Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
Vinayak Gupta, Srikanta Bedathur, Abir De
Temporal sequences have become pervasive in various real-world applications. Consequently, the volume of data generated in the form of continuous time-event sequence(s) or CTES(s)…
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
Vinayak Gupta
With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-ti…
Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs
Vinayak Gupta, Srikanta Bedathur
Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, et…
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes
Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya +1
A large fraction of data generated via human activities such as online purchases, health records, spatial mobility etc. can be represented as a sequence of events over a continuous…
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
Vinayak Gupta, Srikanta Bedathur
Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike machine-made time series, these action sequences are highly disp…
Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
Vinayak Gupta, Srikanta Bedathur, Abir De
Recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving conti…
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer
Vinayak Gupta, Srikanta Bedathur
Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective locatio…
Learning Neural Models for Continuous-Time Sequences
Vinayak Gupta
The large volumes of data generated by human activities such as online purchases, health records, spatial mobility etc. are stored as a sequence of events over a continuous time. L…
Region Invariant Normalizing Flows for Mobility Transfer
Vinayak Gupta, Srikanta Bedathur
There exists a high variability in mobility data volumes across different regions, which deteriorates the performance of spatial recommender systems that rely on region-specific da…
BERT Meets Relational DB: Contextual Representations of Relational Databases
Siddhant Arora, Vinayak Gupta, Garima Gaur +1
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semanti…
Modeling Implicit Communities using Spatio-Temporal Point Processes from Geo-tagged Event Traces
Ankita Likhyani, Vinayak Gupta, Srijith P. K. +2
The location check-ins of users through various location-based services such as Foursquare, Twitter, and Facebook Places, etc., generate large traces of geo-tagged events. These ev…