NDLI: Learning programs from noisy data

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Programming the world of uncertain things (keynote)

Breaking through the normalization barrier: a self-interpreter for f-omega

Temporal verification of higher-order functional programs

Query-guided maximum satisfiability

Fully-abstract compilation by approximate back-translation

Decidability of inferring inductive invariants

Dependent types and multi-monadic effects in F*

Automatic patch generation by learning correct code

Chapar: certified causally consistent distributed key-value stores

Principal type inference for GADTs

Combining static analysis with probabilistic models to enable market-scale Android inter-component analysis

SMO: an integrated approach to intra-array and inter-array storage optimization

Effects as sessions, sessions as effects

Modelling the ARMv8 architecture, operationally: concurrency and ISA

Newtonian program analysis via tensor product

Lattice-theoretic progress measures and coalgebraic model checking

Learning programs from noisy data

Synthesis of reactive controllers for hybrid systems (keynote)

Type theory in type theory using quotient inductive types

Scaling network verification using symmetry and surgery

String solving with word equations and transducers: towards a logic for analysing mutation XSS

Lightweight verification of separate compilation

The hardness of data packing

Fabular: regression formulas as probabilistic programming

Estimating types in binaries using predictive modeling

'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

Abstracting gradual typing

Abstraction refinement guided by a learnt probabilistic model

PolyCheck: dynamic verification of iteration space transformations on affine programs

Monitors and blame assignment for higher-order session types

A concurrency semantics for relaxed atomics that permits optimisation and avoids thin-air executions

Casper: an efficient approach to call trace collection

Algorithms for algebraic path properties in concurrent systems of constant treewidth components

Optimizing synthesis with metasketches

Confluences in programming languages research (keynote)

System f-omega with equirecursive types for datatype-generic programming

Model checking for symbolic-heap separation logic with inductive predicates

Symbolic computation of differential equivalences

From MinX to MinC: semantics-driven decompilation of recursive datatypes

The complexity of interaction

Kleenex: compiling nondeterministic transducers to deterministic streaming transducers

Algorithmic analysis of qualitative and quantitative termination problems for affine probabilistic programs

A program logic for concurrent objects under fair scheduling

The gradualizer: a methodology and algorithm for generating gradual type systems

Learning invariants using decision trees and implication counterexamples

Printing floating-point numbers: a faster, always correct method

Environmental bisimulations for probabilistic higher-order languages

Overhauling SC atomics in C11 and OpenCL

Pushdown control-flow analysis for free

Memoryful geometry of Interaction II: recursion and adequacy

Maximal specification synthesis

A theory of effects and resources: adjunction models and polarised calculi

Reducing crash recoverability to reachability

Unboundedness and downward closures of higher-order pushdown automata

Sound type-dependent syntactic language extension

Transforming spreadsheet data types using examples

PSync: a partially synchronous language for fault-tolerant distributed algorithms

Is sound gradual typing dead?

Symbolic abstract data type inference

Taming release-acquire consistency

Binding as sets of scopes

Example-directed synthesis: a type-theoretic interpretation

Learning programs from noisy data

Content Provider	ACM Digital Library
Author	Vechev, Martin Raychev, Veselin Bielik, Pavol Krause, Andreas
Abstract	We present a new approach for learning programs from noisy datasets. Our approach is based on two new concepts: a regularized program generator which produces a candidate program based on a small sample of the entire dataset while avoiding overfitting, and a dataset sampler which carefully samples the dataset by leveraging the candidate program's score on that dataset. The two components are connected in a continuous feedback-directed loop. We show how to apply this approach to two settings: one where the dataset has a bound on the noise, and another without a noise bound. The second setting leads to a new way of performing approximate empirical risk minimization on hypotheses classes formed by a discrete search space. We then present two new kinds of program synthesizers which target the two noise settings. First, we introduce a novel regularized bitstream synthesizer that successfully generates programs even in the presence of incorrect examples. We show that the synthesizer can detect errors in the examples while combating overfitting -- a major problem in existing synthesis techniques. We also show how the approach can be used in a setting where the dataset grows dynamically via new examples (e.g., provided by a human). Second, we present a novel technique for constructing statistical code completion systems. These are systems trained on massive datasets of open source programs, also known as ``Big Code''. The key idea is to introduce a domain specific language (DSL) over trees and to learn functions in that DSL directly from the dataset. These learned functions then condition the predictions made by the system. This is a flexible and powerful technique which generalizes several existing works as we no longer need to decide a priori on what the prediction should be conditioned (another benefit is that the learned functions are a natural mechanism for explaining the prediction). As a result, our code completion system surpasses the prediction capabilities of existing, hard-wired systems. .
Starting Page	761
Ending Page	774
Page Count	14
File Format	PDF
ISBN	9781450335492
DOI	10.1145/2837614.2837671
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-01-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical code completion Noisy data Big code Program synthesis Regularization Anomaly detection
Content Type	Text
Resource Type	Article

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