1.3k citations · 2.1k across the 35 of their papers we have counts for
15 papers · 1 filter
Inference Plans for Hybrid Particle Filtering
Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart +2
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These…
The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation
Charles Yuan, Michael Carbin
Numerous quantum algorithms require the use of quantum error correction to overcome the intrinsic unreliability of physical qubits. However, error correction imposes a unique perfo…
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software dev…
Verifying Performance Properties of Probabilistic Inference
Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart +2
In this extended abstract, we discuss the opportunity to formally verify that inference systems for probabilistic programming guarantee good performance. In particular, we focus on…
Quantum Control Machine: The Limits of Control Flow in Quantum Programming
Charles Yuan, Agnes Villanyi, Michael Carbin
Quantum algorithms for tasks such as factorization, search, and simulation rely on control flow such as branching and iteration that depends on the value of data in superposition.…
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming
Eric Atkinson, Charles Yuan, Guillaume Baudart +2
Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on…
Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs
Charles Yuan, Christopher McNally, Michael Carbin
Quantum programming languages enable developers to implement algorithms for quantum computers that promise computational breakthroughs in classically intractable tasks. Programming…
Tower: Data Structures in Quantum Superposition
Charles Yuan, Michael Carbin
Emerging quantum algorithms for problems such as element distinctness, subset sum, and closest pair demonstrate computational advantages by relying on abstract data structures. Pra…
Programming with Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Surrogates, models that mimic the behavior of programs, form the basis of a variety of development workflows. We study three surrogate-based design patterns, evaluating each in cas…
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams
Eric Atkinson, Guillaume Baudart, Louis Mandel +2
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated…
Simplifying Dependent Reductions in the Polyhedral Model
Cambridge Yang, Eric Atkinson, Michael Carbin
A Reduction -- an accumulation over a set of values, using an associative and commutative operator -- is a common computation in many numerical computations, including scientific c…
: Computable Semantics for Differentiable Programming with Higher-Order Functions and Datatypes
Benjamin Sherman, Jesse Michel, Michael Carbin
Deep learning is moving towards increasingly sophisticated optimization objectives that employ higher-order functions, such as integration, continuous optimization, and root-findin…
Reactive Probabilistic Programming
Guillaume Baudart, Louis Mandel, Eric Atkinson +3
Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or Scade used routinely for implementing safety critical control software, e.g., fly-by-wire and…
Verifying Programs Under Custom Application-Specific Execution Models
Brett Boston, Zoe Gong, Michael Carbin
Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additi…
Verifying Handcoded Probabilistic Inference Procedures
Eric Atkinson, Cambridge Yang, Michael Carbin
Researchers have recently proposed several systems that ease the process of performing Bayesian probabilistic inference. These include systems for automatic inference algorithm syn…