Vikash K. Mansinghka — accepted AI-conference papers

2023

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation

Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla +2

ICCV 2023poster

The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understanding that aims to infer the 3D scene structure from 2D images. In this paper, we introduce probabilistic modeling to the i…

Cited by 4 PDF ↗code ↗Scholar ↗

2023

ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images

Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka +1

AISTATS 2023poster

The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge there may still be uncertainty about the shapes of occluded parts of objects. Recently, conditional neural radiance field (…

Cited by 18 Source ↗PDF ↗code ↗Scholar ↗

2023

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals

Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell +1

AISTATS 2023poster

This paper introduces SMCP3, a method for automatically implementing custom sequential Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and resample-move SMC (Gilks and Berzuini, 2001), SMCP3 algorithms can improve the quality of samples and weights using pairs o…

Cited by 21 Source ↗PDF ↗code ↗Scholar ↗

2022

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model

Nishad Gothoskar, Miguel Lazaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka +1

ICRA 2022poster

Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods require either precise calibration of the robot kinematic model and cameras or use neural architectures that require large amounts of data to train. In t…

Cited by 2 Source ↗Scholar ↗

2022

Recursive Monte Carlo and variational inference with auxiliary variables

Alexander K. Lew, Marco Cusumano-Towner, Vikash K. Mansinghka

UAI 2022poster

A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational families. This takes many interesting proposals off the table, such as those based o…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗

2021

Hierarchical infinite relational model

Feras A. Saad, Vikash K. Mansinghka

UAI 2021poster

This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations usin…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗

2020

Online Bayesian Goal Inference for Boundedly Rational Planning Agents

Tan Zhi-Xuan, Jordyn Mann, Tom Silver, Josh Tenenbaum, Vikash K. Mansinghka

NeurIPS 2020poster

People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when we detect that they might not achieve their goals. How might we endow machines with similar capabilities? Here we prese…

Cited by 127 Source ↗PDF ↗Scholar ↗

2019

A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions

Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka

AISTATS 2019poster

The objective of goodness-of-fit testing is to assess whether a dataset of observations is likely to have been drawn from a candidate probability distribution. This paper presents a rank-based family of goodness-of-fit tests that is specialized to discrete distributions on high-dimensional domains.…

Cited by 10 Source ↗PDF ↗Scholar ↗

2017

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms

Marco Cusumano-Towner, Vikash K Mansinghka

NeurIPS 2017poster

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of…

Cited by 21 Source ↗PDF ↗Scholar ↗

2016

A Probabilistic Programming Approach To Probabilistic Data Analysis

Feras Saad, Vikash K Mansinghka

NeurIPS 2016poster

Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and…

Cited by 27 Source ↗PDF ↗Scholar ↗