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Iwata Y.,University for Information Science and Technology | Oka K.,Tokyo University of Information Sciences | Yoshida Y.,Preferred Infrastructure
Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms | Year: 2014

In the area of parameterized complexity, to cope with NP- Hard problems, we introduce a parameter k besides the input size n, and we aim to design algorithms (called FPT algorithms) that run in 0(f(k)nd) time for some function f(k) and constant d. Though FPT algorithms have been successfully designed for many problems, typically they are not sufficiently fast because of huge f(k) and d. In this paper, we give FPT algorithms with small f(k) and d for many important problems including Odd Cycle Transversal and Almost 2-SAT. More specifically, we can choose f(k) as a single exponential (4k) and d as one, that is, linear in the input size. To the best of our knowledge, our algorithms achieve linear time complexity for the first time for these problems. To obtain our algorithms for these problems, we consider a large class of integer programs, called BIP2. Then we show that, in linear time, we can reduce BIP2 to Vertex Cover Above LP preserving the parameter k, and we can compute an optimal LP solution for Vertex Cover Above LP using network flow. Then, we perform an exaustive search by fixing half- integral values in the optimal LP solution for Vertex Cover Above LP. A bottleneck here is that we need to recompute an LP optimal solution after branching. To address this issue, we exploit network flow to update the optimal LP solution in linear time. Copyright © 2014 by the Society for Industrial and Applied Mathematics.

News Article | December 17, 2015

« Ford and Corning introduce lightweight Gorilla Glass hybrid windshield technology on Ford GT | Main | SwRI-led AC2AT consortium launches second year of advanced emissions research with focus on improving aftertreatment and fuel efficiency strategies » With the aim of promoting the joint research and development of artificial intelligence technologies in mobility-related fields, Toyota Motor Corporation (TMC) has decided to invest in Preferred Networks Inc. (PFN). The investment will amount to ¥1 billion (US$8.2 million), and Toyota will receive stock in PFN through the allocation of new shares to a third party as of 30 December 2015. TMC is actively researching and developing a wide-range of technologies that are expected to significantly affect the nature of mobility in the future—including automated driving technologies—with a particular focus on intelligence that assists driving, connectivity and interactivity. (Earlier post.) With this shared purpose in mind, PFN would like to plan and develop new products and services by leveraging its expertise and unique technological capabilities in the field of artificial intelligence, including adopting and expanding on its cutting-edge machine and deep learning technologies in industrial applications. With this investment, the collaboration between PFN and TMC in mobility-related businesses will be strengthened. TMC plans to carry out a demonstration of distributed machine learning at CES (Consumer Electronics Show) 2016 in Las Vegas, which will run from 6-9 January. The demonstration will showcase a concept that features a potential automotive application of PFN’s technology, where multiple vehicles learn not to collide with each other. PFN is a venture firm with world-class technological capabilities in the fields of natural language processing and machine learning. The firm was established in March 2014 as a spin-off from Preferred Infrastructure, Inc. PFN’s aim is to apply real-time machine learning technologies to new applications in the emerging field of the Internet of Things. In November, TMC announced that it was establishing a new company, Toyota Research Institute Inc. (TRI), as an R&D enterprise with an initial focus on artificial intelligence and robotics.

Blais E.,Carnegie Mellon University | Weinstein A.,Tel Aviv University | Yoshida Y.,Preferred Infrastructure
Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS | Year: 2012

Given a Boolean function f, the f-isomorphism testing problem requires a randomized algorithm to distinguish functions that are identical to f up to relabeling of the input variables from functions that are far from being so. An important open question in property testing is to determine for which functions f we can test f-isomorphism with a constant number of queries. Despite much recent attention to this question, essentially only two classes of functions were known to be efficiently isomorphism testable: symmetric functions and juntas. We unify and extend these results by showing that all partially symmetric functions - functions invariant to the reordering of all but a constant number of their variables - are efficiently isomorphism-testable. This class of functions, first introduced by Shannon, includes symmetric functions, juntas, and many other functions as well. We conjecture that these functions are essentially the only functions efficiently isomorphism-testable. To prove our main result, we also show that partial symmetry is efficiently testable. In turn, to prove this result we had to revisit the junta testing problem. We provide a new proof of correctness of the nearly-optimal junta tester. Our new proof replaces the Fourier machinery of the original proof with a purely combinatorial argument that exploits the connection between sets of variables with low influence and intersecting families. Another important ingredient in our proofs is a new notion of symmetric influence. We use this measure of influence to prove that partial symmetry is efficiently testable and also to construct an efficient sample extractor for partially symmetric functions. We then combine the sample extractor with the testing-by-implicit-learning approach to complete the proof that partially symmetric functions are efficiently isomorphism-testable. © 2012 IEEE.

Akiba T.,University of Tokyo | Iwata Y.,University of Tokyo | Yoshida Y.,Preferred Infrastructure
Proceedings of the ACM SIGMOD International Conference on Management of Data | Year: 2013

We propose a new exact method for shortest-path distance queries on large-scale networks. Our method precomputes distance labels for vertices by performing a breadth-first search from every vertex. Seemingly too obvious and too inefficient at first glance, the key ingredient introduced here is pruning during breadth-first searches. While we can still answer the correct distance for any pair of vertices from the labels, it surprisingly reduces the search space and sizes of labels. Moreover, we show that we can perform 32 or 64 breadth-first searches simultaneously exploiting bitwise operations. We experimentally demonstrate that the combination of these two techniques is efficient and robust on various kinds of large-scale real-world networks. In particular, our method can handle social networks and web graphs with hundreds of millions of edges, which are two orders of magnitude larger than the limits of previous exact methods, with comparable query time to those of previous methods. [2]. We would also like to thank the anonymous reviewers for their constructive suggestions on improving the paper. Yuichi Yoshida is supported by JSPS Grant-in-Aid for Research Activity Start-up (24800082), MEXT Grant-in-Aid for Scientific Research on Innovative Areas (24106001), and JST, ERATO, Kawarabayashi Large Graph Project. Copyright © 2013 ACM.

Yoshida Y.,Preferred Infrastructure | Zhou Y.,Carnegie Mellon University
ITCS 2014 - Proceedings of the 2014 Conference on Innovations in Theoretical Computer Science | Year: 2014

We consider approximation schemes for the maximum constraint satisfaction problems and the maximum assignment problems. Though they are NP-Hard in general, if the instance is "dense" or "locally dense", then they are known to have approximation schemes that run in polynomial time or quasi-polynomial time. In this paper, we give a unified method of showing these approximation schemes based on the Sherali-Adams linear programming relaxation hierarchy. We also use our linear programming-based framework to show new algorithmic results on the optimization version of the hypergraph isomorphism problem. Copyright 2014 ACM.

Yoshida Y.,Preferred Infrastructure
Proceedings of the Annual ACM Symposium on Theory of Computing | Year: 2011

Raghavendra (STOC 2008) gave an elegant and surprising result: if Khot's Unique Games Conjecture (STOC 2002) is true, then for every constraint satisfaction problem (CSP), the best approximation ratio is attained by a certain simple semidefinite programming and a rounding scheme for it. In this paper, we show that similar results hold for constant-time approximation algorithms in the bounded-degree model. Specifically, we present the following: (i) For every CSP, we construct an oracle that serves an access, in constant time, to a nearly optimal solution to a basic LP relaxation of the CSP. (ii) Using the oracle, we give a constant-time rounding scheme that achieves an approximation ratio coincident with the integrality gap of the basic LP. (iii) Finally, we give a generic conversion from integrality gaps of basic LPs to hardness results. All of those results are unconditional. Therefore, for every bounded-degree CSP, we give the best constant-time approximation algorithm among all. A CSP instance is called ε-far from satisfiability if we must remove at least an ε-fraction of constraints to make it satisfiable. A CSP is called testable if there is a constant-time algorithm that distinguishes satisfiable instances from ε-far instances with probability at least 2/3. Using the results above, we also derive, under a technical assumption, an equivalent condition under which a CSP is testable in the bounded-degree model. © 2011 ACM.

Yoshida Y.,Preferred Infrastructure
Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms | Year: 2016

Let {fi : Fip → {0, 1}} be a sequence of functions, where p is a fixed prime and Fp is the finite field of order p. The limit of the sequence can be syntactically defined using the notion of ultralimit. Inspired by the Gowers norm, we introduce a metric over limits of function sequences, and study properties of it. One application of this metric is that it provides a simpler characterization of affine-invariant parameters of functions that are constant-query estimable than the previous one obtained by Yoshida (STOC'14). Using this characterization, we show that the property of being a function of a constant number of low-degree polynomials and a constant number of factored polynomials (of arbitrary degrees) is constant-query testable if it is closed under blowing-up. Examples of this property include the property of having a constant spectral norm and degree-structural properties with rank conditions. © Copyright (2016) by SIAM: Society for Industrial and Applied Mathematics.

Yoshida Y.,Preferred Infrastructure
Proceedings of the Annual ACM Symposium on Theory of Computing | Year: 2014

Let P be a property of function Fp n → {0; 1} for a fixed prime p. An algorithm is called a tester for P if, given a query access to the input function f, with high probability, it accepts when f satisfies P and rejects when f is "far" from satisfying P. In this paper, we give a characterization of affine-invariant properties that are (two-sided error) testable with a constant number of queries. The characterization is stated in terms of decomposition theorems, which roughly claim that any function can be decomposed into a structured part that is a function of a constant number of polynomials, and a pseudo-random part whose Gowers norm is small. We first give an algorithm that tests whether the structured part of the input function has a specific form. Then we show that an affine-invariant property is testable with a constant number of queries if and only if it can be reduced to the problem of testing whether the structured part of the input function is close to one of a constant number of candidates. © 2014 ACM.

Yoshida Y.,Preferred Infrastructure
Proceedings of the Annual IEEE Conference on Computational Complexity | Year: 2011

In this paper, we consider lower bounds on the query complexity for testing CSPs in the bounded-degree model. We mainly consider Boolean CSPs allowing literals. First, for any "symmetric" predicate P: {0,1}k → {0,1} except EQU where k ≥ 3, we show that every (randomized) algorithm that distinguishes satisfiable instances of CSP(P) from instances (|P-1(0)|/2k-ε)-far from satisfiability requires Ω(n1/2+δ) queries where n is the number of variables and δ > 0 is a constant that depends on P and e. This breaks a natural lower bound Ω(n1/2), which is obtained by the birthday paradox. We also show that every one-sided error tester requires Ω(n) queries for such P. These results are hereditary in the sense that the same results hold for any predicate Q such that P-1(1) ⊆ Q-1(1). For EQU, we give a one-sided error tester whose query complexity is Õ(n1/2). Also, for 2-XOR (or, equivalently E2LIN2), we show an Ω(n1/2+δ) lower bound for distinguishing instances between ε-close to and (1/2 - ε)-far from satisfiability. Next, for the general k-CSP over the binary domain, we show that every algorithm that distinguishes satisfiable instances from instances (1 - 2k/2k - ε)-far from satisfiability requires Ω(n) queries. The matching NP-hardness is not known, even assuming the Unique Games Conjecture or the d-to-1 Conjecture. As a corollary, for Maximum Independent Set on graphs with n vertices and a degree bound d, we show that every approximation algorithm within a factor d/poly log d and an additive error of en requires Ω(n) queries. Previously, only super-constant lower bounds were known. © 2011 IEEE.

Preferred Infrastructure | Date: 2014-10-15

The information processing device according to one embodiment comprises a storage device for storing model information generated through execution of machine learning while employing learning data, the model information including feature information and weighting information associated with the feature information, for each of a plurality of labels; and a display control device for displaying the feature information included in the model information for at least one label among the plurality of labels on a display device, on the basis of the weighting information associated with the feature information.

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