10,579 research outputs found
Distribution-Independent Evolvability of Linear Threshold Functions
Valiant's (2007) model of evolvability models the evolutionary process of
acquiring useful functionality as a restricted form of learning from random
examples. Linear threshold functions and their various subclasses, such as
conjunctions and decision lists, play a fundamental role in learning theory and
hence their evolvability has been the primary focus of research on Valiant's
framework (2007). One of the main open problems regarding the model is whether
conjunctions are evolvable distribution-independently (Feldman and Valiant,
2008). We show that the answer is negative. Our proof is based on a new
combinatorial parameter of a concept class that lower-bounds the complexity of
learning from correlations.
We contrast the lower bound with a proof that linear threshold functions
having a non-negligible margin on the data points are evolvable
distribution-independently via a simple mutation algorithm. Our algorithm
relies on a non-linear loss function being used to select the hypotheses
instead of 0-1 loss in Valiant's (2007) original definition. The proof of
evolvability requires that the loss function satisfies several mild conditions
that are, for example, satisfied by the quadratic loss function studied in
several other works (Michael, 2007; Feldman, 2009; Valiant, 2010). An important
property of our evolution algorithm is monotonicity, that is the algorithm
guarantees evolvability without any decreases in performance. Previously,
monotone evolvability was only shown for conjunctions with quadratic loss
(Feldman, 2009) or when the distribution on the domain is severely restricted
(Michael, 2007; Feldman, 2009; Kanade et al., 2010
Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations
Consider the following heuristic for building a decision tree for a function
. Place the most influential variable of
at the root, and recurse on the subfunctions and on the
left and right subtrees respectively; terminate once the tree is an
-approximation of . We analyze the quality of this heuristic,
obtaining near-matching upper and lower bounds:
Upper bound: For every with decision tree size and every
, this heuristic builds a decision tree of size
at most .
Lower bound: For every and , there is an with decision tree size such that
this heuristic builds a decision tree of size .
We also obtain upper and lower bounds for monotone functions:
and
respectively. The lower bound disproves conjectures of Fiat and Pechyony (2004)
and Lee (2009).
Our upper bounds yield new algorithms for properly learning decision trees
under the uniform distribution. We show that these algorithms---which are
motivated by widely employed and empirically successful top-down decision tree
learning heuristics such as ID3, C4.5, and CART---achieve provable guarantees
that compare favorably with those of the current fastest algorithm (Ehrenfeucht
and Haussler, 1989). Our lower bounds shed new light on the limitations of
these heuristics.
Finally, we revisit the classic work of Ehrenfeucht and Haussler. We extend
it to give the first uniform-distribution proper learning algorithm that
achieves polynomial sample and memory complexity, while matching its
state-of-the-art quasipolynomial runtime
CoLoR: a Coq library on well-founded rewrite relations and its application to the automated verification of termination certificates
Termination is an important property of programs; notably required for
programs formulated in proof assistants. It is a very active subject of
research in the Turing-complete formalism of term rewriting systems, where many
methods and tools have been developed over the years to address this problem.
Ensuring reliability of those tools is therefore an important issue. In this
paper we present a library formalizing important results of the theory of
well-founded (rewrite) relations in the proof assistant Coq. We also present
its application to the automated verification of termination certificates, as
produced by termination tools
The Stable Roommates problem with short lists
We consider two variants of the classical Stable Roommates problem with
Incomplete (but strictly ordered) preference lists SRI that are degree
constrained, i.e., preference lists are of bounded length. The first variant,
EGAL d-SRI, involves finding an egalitarian stable matching in solvable
instances of SRI with preference lists of length at most d. We show that this
problem is NP-hard even if d=3. On the positive side we give a
(2d+3)/7-approximation algorithm for d={3,4,5} which improves on the known
bound of 2 for the unbounded preference list case. In the second variant of
SRI, called d-SRTI, preference lists can include ties and are of length at most
d. We show that the problem of deciding whether an instance of d-SRTI admits a
stable matching is NP-complete even if d=3. We also consider the "most stable"
version of this problem and prove a strong inapproximability bound for the d=3
case. However for d=2 we show that the latter problem can be solved in
polynomial time.Comment: short version appeared at SAGT 201
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