5 Search Results for "Singh, Rishabh"

Document
Invited Talk
DOI: 10.4230/LIPIcs.FSCD.2025.2
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk)

Authors: Matthew L. Daggitt, Wen Kokke, Robert Atkey, Ekaterina Komendantskaya, Natalia Slusarz, and Luca Arnaboldi

Published in: LIPIcs, Volume 337, 10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025)

Abstract
Neuro-symbolic programs, i.e. programs containing both machine learning components and traditional symbolic code, are becoming increasingly widespread. Finding a general methodology for verifying such programs is challenging due to both the number of different tools involved and the intricate interface between the "neural" and "symbolic" program components. In this paper we present a general decomposition of the neuro-symbolic verification problem into parts, and examine the problem of the embedding gap that occurs when one tries to combine proofs about the neural and symbolic components. To address this problem we then introduce Vehicle - standing as an abbreviation for a "verification condition language" - an intermediate programming language interface between machine learning frameworks, automated theorem provers, and dependently-typed formalisations of neuro-symbolic programs. Vehicle allows users to specify the properties of the neural components of neuro-symbolic programs once, and then safely compile the specification to each interface using a tailored typing and compilation procedure. We give a high-level overview of Vehicle’s overall design, its interfaces and compilation & type-checking procedures, and then demonstrate its utility by formally verifying the safety of a simple autonomous car controlled by a neural network, operating in a stochastic environment with imperfect information.
Cite as

Matthew L. Daggitt, Wen Kokke, Robert Atkey, Ekaterina Komendantskaya, Natalia Slusarz, and Luca Arnaboldi. Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk). In 10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 337, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{daggitt_et_al:LIPIcs.FSCD.2025.2,
  author =	{Daggitt, Matthew L. and Kokke, Wen and Atkey, Robert and Komendantskaya, Ekaterina and Slusarz, Natalia and Arnaboldi, Luca},
  title =	{{Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs}},
  booktitle =	{10th International Conference on Formal Structures for Computation and Deduction (FSCD 2025)},
  pages =	{2:1--2:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-374-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{337},
  editor =	{Fern\'{a}ndez, Maribel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSCD.2025.2},
  URN =		{urn:nbn:de:0030-drops-236172},
  doi =		{10.4230/LIPIcs.FSCD.2025.2},
  annote =	{Keywords: Neural Network Verification, Types, Interactive Theorem Provers}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.102
Fourier Analysis of Iterative Algorithms

Authors: Chris Jones and Lucas Pesenti

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
We study a general class of nonlinear iterative algorithms which includes power iteration, belief propagation and approximate message passing, and many forms of gradient descent. When the input is a random matrix with i.i.d. entries, we use Boolean Fourier analysis to analyze these algorithms as low-degree polynomials in the entries of the input matrix. Each symmetrized Fourier character represents all monomials with a certain shape as specified by a small graph, which we call a Fourier diagram. We prove fundamental asymptotic properties of the Fourier diagrams: over the randomness of the input, all diagrams with cycles are negligible; the tree-shaped diagrams form a basis of asymptotically independent Gaussian vectors; and, when restricted to the trees, iterative algorithms exactly follow an idealized Gaussian dynamic. We use this to prove a state evolution formula, giving a "complete" asymptotic description of the algorithm’s trajectory. The restriction to tree-shaped monomials mirrors the assumption of the cavity method, a 40-year-old non-rigorous technique in statistical physics which has served as one of the most important techniques in the field. We demonstrate how to implement cavity method derivations by 1) restricting the iteration to its tree approximation, and 2) observing that heuristic cavity method-type arguments hold rigorously on the simplified iteration. Our proofs use combinatorial arguments similar to the trace method from random matrix theory. Finally, we push the diagram analysis to a number of iterations that scales with the dimension n of the input matrix, proving that the tree approximation still holds for a simple variant of power iteration all the way up to n^{Ω(1)} iterations.
Cite as

Chris Jones and Lucas Pesenti. Fourier Analysis of Iterative Algorithms. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 102:1-102:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jones_et_al:LIPIcs.ICALP.2025.102,
  author =	{Jones, Chris and Pesenti, Lucas},
  title =	{{Fourier Analysis of Iterative Algorithms}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{102:1--102:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.102},
  URN =		{urn:nbn:de:0030-drops-234791},
  doi =		{10.4230/LIPIcs.ICALP.2025.102},
  annote =	{Keywords: Iterative Algorithms, Message-passing Algorithms, Random Matrix Theory}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.10
Bottom-Up Synthesis of Memory Mutations with Separation Logic

Authors: Kasra Ferdowsi and Hila Peleg

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Programming-by-Example (PBE) is the paradigm of program synthesis specified via input-output pairs. It is commonly used because examples are easy to provide and collect from the environment. A popular optimization for enumerative synthesis with examples is Observational Equivalence (OE), which groups programs into equivalence classes according to their evaluation on example inputs. Current formulations of OE, however, are severely limited by the assumption that the synthesizer’s target language contains only pure components with no side-effects, either enforcing this in their target language, or ignoring it, leading to an incorrect enumeration. This limits their ability to use realistic component sets. We address this limitation by borrowing from Separation Logic, which can compositionally reason about heap mutations. We reformulate PBE using a restricted Separation Logic: Concrete Heap Separation Logic (CHSL), transforming the search for programs into a proof search in CHSL. This lets us perform bottom-up enumerative synthesis without the need for expert-provided annotations or domain-specific inferences, but with three key advantages: we (i) preserve correctness in the presence of memory-mutating operations, (ii) compact the search space by representing many concrete programs as one under CHSL, and (iii) perform a provably correct OE-reduction. We present SObEq (Side-effects in OBservational EQuivalence), a bottom-up enumerative algorithm that, given a PBE task, searches for its CHSL derivation. The SObEq algorithm is proved correct with no purity assumptions: we show it is guaranteed to lose no solutions. We also evaluate our implementation of SObEq on benchmarks from the literature and online sources, and show that it produces high-quality results quickly.
Cite as

Kasra Ferdowsi and Hila Peleg. Bottom-Up Synthesis of Memory Mutations with Separation Logic. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 10:1-10:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ferdowsi_et_al:LIPIcs.ECOOP.2025.10,
  author =	{Ferdowsi, Kasra and Peleg, Hila},
  title =	{{Bottom-Up Synthesis of Memory Mutations with Separation Logic}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{10:1--10:32},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.10},
  URN =		{urn:nbn:de:0030-drops-233036},
  doi =		{10.4230/LIPIcs.ECOOP.2025.10},
  annote =	{Keywords: Program synthesis, observational equivalence}
}
Document
DOI: 10.4230/DagRep.7.9.86
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382)

Authors: Ute Schmid, Stephen H. Muggleton, and Rishabh Singh

Published in: Dagstuhl Reports, Volume 7, Issue 9 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17382 "Approaches and Applications of Inductive Programming". After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given.
Cite as

Ute Schmid, Stephen H. Muggleton, and Rishabh Singh. Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). In Dagstuhl Reports, Volume 7, Issue 9, pp. 86-108, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{schmid_et_al:DagRep.7.9.86,
  author =	{Schmid, Ute and Muggleton, Stephen H. and Singh, Rishabh},
  title =	{{Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382)}},
  pages =	{86--108},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{9},
  editor =	{Schmid, Ute and Muggleton, Stephen H. and Singh, Rishabh},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.9.86},
  URN =		{urn:nbn:de:0030-drops-85909},
  doi =		{10.4230/DagRep.7.9.86},
  annote =	{Keywords: inductive program synthesis, inductive logic programming, probabilistic programming, end-user programming, human-like computing}
}
Document
DOI: 10.4230/LIPIcs.SNAPL.2017.16
AP: Artificial Programming

Authors: Rishabh Singh and Pushmeet Kohli

Published in: LIPIcs, Volume 71, 2nd Summit on Advances in Programming Languages (SNAPL 2017)

Abstract
The ability to automatically discover a program consistent with a given user intent (specification) is the holy grail of Computer Science. While significant progress has been made on the so-called problem of Program Synthesis, a number of challenges remain; particularly for the case of synthesizing richer and larger programs. This is in large part due to the difficulty of search over the space of programs. In this paper, we argue that the above-mentioned challenge can be tackled by learning synthesizers automatically from a large amount of training data. We present a first step in this direction by describing our novel synthesis approach based on two neural architectures for tackling the two key challenges of Learning to understand partial input-output specifications and Learning to search programs. The first neural architecture called the Spec Encoder computes a continuous representation of the specification, whereas the second neural architecture called the Program Generator incrementally constructs programs in a hypothesis space that is conditioned by the specification vector. The key idea of the approach is to train these architectures using a large set of (spec,P) pairs, where P denotes a program sampled from the DSL L and spec denotes the corresponding specification satisfied by P. We demonstrate the effectiveness of our approach on two preliminary instantiations. The first instantiation, called Neural FlashFill, corresponds to the domain of string manipulation programs similar to that of FlashFill. The second domain considers string transformation programs consisting of composition of API functions. We show that a neural system is able to perform quite well in learning a large majority of programs from few input-output examples. We believe this new approach will not only dramatically expand the applicability and effectiveness of Program Synthesis, but also would lead to the coming together of the Program Synthesis and Machine Learning research disciplines.
Cite as

Rishabh Singh and Pushmeet Kohli. AP: Artificial Programming. In 2nd Summit on Advances in Programming Languages (SNAPL 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 71, pp. 16:1-16:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{singh_et_al:LIPIcs.SNAPL.2017.16,
  author =	{Singh, Rishabh and Kohli, Pushmeet},
  title =	{{AP: Artificial Programming}},
  booktitle =	{2nd Summit on Advances in Programming Languages (SNAPL 2017)},
  pages =	{16:1--16:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-032-3},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{71},
  editor =	{Lerner, Benjamin S. and Bod{\'\i}k, Rastislav and Krishnamurthi, Shriram},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SNAPL.2017.16},
  URN =		{urn:nbn:de:0030-drops-71284},
  doi =		{10.4230/LIPIcs.SNAPL.2017.16},
  annote =	{Keywords: Neural Program Synthesis, Neural Programming, Neural FlashFill}
}
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