2 Search Results for "Amir, Eyal"

Document
DOI: 10.4230/LIPIcs.ITCS.2021.41
Interactive Proofs for Verifying Machine Learning

Authors: Shafi Goldwasser, Guy N. Rothblum, Jonathan Shafer, and Amir Yehudayoff

Published in: LIPIcs, Volume 185, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)

Abstract
We consider the following question: using a source of labeled data and interaction with an untrusted prover, what is the complexity of verifying that a given hypothesis is "approximately correct"? We study interactive proof systems for PAC verification, where a verifier that interacts with a prover is required to accept good hypotheses, and reject bad hypotheses. Both the verifier and the prover are efficient and have access to labeled data samples from an unknown distribution. We are interested in cases where the verifier can use significantly less data than is required for (agnostic) PAC learning, or use a substantially cheaper data source (e.g., using only random samples for verification, even though learning requires membership queries). We believe that today, when data and data-driven algorithms are quickly gaining prominence, the question of verifying purported outcomes of data analyses is very well-motivated. We show three main results. First, we prove that for a specific hypothesis class, verification is significantly cheaper than learning in terms of sample complexity, even if the verifier engages with the prover only in a single-round (NP-like) protocol. Moreover, for this class we prove that single-round verification is also significantly cheaper than testing closeness to the class. Second, for the broad class of Fourier-sparse boolean functions, we show a multi-round (IP-like) verification protocol, where the prover uses membership queries, and the verifier is able to assess the result while only using random samples. Third, we show that verification is not always more efficient. Namely, we show a class of functions where verification requires as many samples as learning does, up to a logarithmic factor.
Cite as

Shafi Goldwasser, Guy N. Rothblum, Jonathan Shafer, and Amir Yehudayoff. Interactive Proofs for Verifying Machine Learning. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 41:1-41:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{goldwasser_et_al:LIPIcs.ITCS.2021.41,
  author =	{Goldwasser, Shafi and Rothblum, Guy N. and Shafer, Jonathan and Yehudayoff, Amir},
  title =	{{Interactive Proofs for Verifying Machine Learning}},
  booktitle =	{12th Innovations in Theoretical Computer Science Conference (ITCS 2021)},
  pages =	{41:1--41:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-177-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{185},
  editor =	{Lee, James R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.41},
  URN =		{urn:nbn:de:0030-drops-135806},
  doi =		{10.4230/LIPIcs.ITCS.2021.41},
  annote =	{Keywords: PAC learning, Fourier analysis of boolean functions, Complexity gaps, Complexity lower bounds, Goldreich-Levin algorithm, Kushilevitz-Mansour algorithm, Distribution testing}
}
Document
DOI: 10.4230/DagSemProc.10081.6
Combining Planning and Motion Planning

Authors: Jaesik Choi and Eyal Amir

Published in: Dagstuhl Seminar Proceedings, Volume 10081, Cognitive Robotics (2010)

Abstract
Robotic manipulation is important for real, physical world applications. General Purpose manipulation with a robot (eg. delivering dishes, opening doors with a key, etc.) is demanding. It is hard because (1) objects are constrained in position and orientation, (2) many non-spatial constraints interact (or interfere) with each other, and (3) robots may have multidegree of freedoms (DOF). In this paper we solve the problem of general purpose robotic manipulation using a novel combination of planning and motion planning. Our approach integrates motions of a robot with other (non-physical or external-to-robot) actions to achieve a goal while manipulating objects. It differs from previous, hierarchical approaches in that (a) it considers kinematic constraints in configuration space (C-space) together with constraints over object manipulations; (b) it automatically generates high-level (logical) actions from a C-space based motion planning algorithm; and (c) it decomposes a planning problem into small segments, thus reducing the complexity of planning.
Cite as

Jaesik Choi and Eyal Amir. Combining Planning and Motion Planning. In Cognitive Robotics. Dagstuhl Seminar Proceedings, Volume 10081, pp. 1-8, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2010)

Copy BibTex To Clipboard

@InProceedings{choi_et_al:DagSemProc.10081.6,
  author =	{Choi, Jaesik and Amir, Eyal},
  title =	{{Combining Planning and Motion Planning}},
  booktitle =	{Cognitive Robotics},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10081},
  editor =	{Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10081.6},
  URN =		{urn:nbn:de:0030-drops-26294},
  doi =		{10.4230/DagSemProc.10081.6},
  annote =	{Keywords: Motion Planning, Factored Planning, Robotic arm}
}
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