55 citations · 74 across the 11 of their papers we have counts for
11 papers
Neural USD: An object-centric framework for iterative editing and control
Alejandro Escontrela, Shrinu Kushagra, Sjoerd van Steenkiste +5
Amazing progress has been made in controllable generative modeling, especially over the last few years. However, some challenges remain. One of them is precise and iterative object…
Model Predictive Simulation Using Structured Graphical Models and Transformers
Xinghua Lou, Meet Dave, Shrinu Kushagra +2
We propose an approach to simulating trajectories of multiple interacting agents (road users) based on transformers and probabilistic graphical models (PGMs), and apply it to the W…
Graph schemas as abstractions for transfer learning, inference, and planning
J. Swaroop Guntupalli, Rajkumar Vasudeva Raju, Shrinu Kushagra +6
Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neur…
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar +4
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalab…
On sampling from data with duplicate records
Alireza Heidari, Shrinu Kushagra, Ihab F. Ilyas
Data deduplication is the task of detecting records in a database that correspond to the same real-world entity. Our goal is to develop a procedure that samples uniformly from the…
Record fusion: A learning approach
Alireza Heidari, George Michalopoulos, Shrinu Kushagra +2
Record fusion is the task of aggregating multiple records that correspond to the same real-world entity in a database. We can view record fusion as a machine learning problem where…
Adjoined Networks: A Training Paradigm with Applications to Network Compression
Utkarsh Nath, Shrinu Kushagra, Yingzhen Yang
Compressing deep neural networks while maintaining accuracy is important when we want to deploy large, powerful models in production and/or edge devices. One common technique used…
Three-dimensional matching is NP-Hard
Shrinu Kushagra
The standard proof of NP-Hardness of 3DM provides a power- reduction of 3SAT to 3DM. In this note, we provide a linear-time reduction. Under the exponential time hypothesis, thi…
Semi-supervised clustering for de-duplication
Shrinu Kushagra, Shai Ben-David, Ihab Ilyas
Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering prob…
Provably noise-robust, regularised -means clustering
Shrinu Kushagra, Yaoliang Yu, Shai Ben-David
We consider the problem of clustering in the presence of noise. That is, when on top of cluster structure, the data also contains a subset of \emph{unstructured} points. Our goal i…
Clustering with Same-Cluster Queries
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to interact with a domain expert, asking whether two given instances bel…