# Tag Info

55

I do not want to sound condescending, but the math you are studying at the undergraduate and even graduate level courses is not advanced. It is the basics. The title of your question should be: Is "basic" math needed/useful in AI research? So, gobble up as much as you can, I have never met a computer scientist who complained about knowing too much math, ...

38

There are a bunch of "no free lunch" theorems in machine learning, roughly stating that there can be no one master learning algorithm that performs uniformly better than all other algorithms (see, e.g., here http://www.no-free-lunch.org/ ). Sure enough, deep learning can be "broken" without much difficulty: http://www.evolvingai.org/fooling Hence, to be ...

26

There are two main gaps in our understanding of neural networks: optimization hardness and generalization performance. Training a neural network requires solving a highly non-convex optimization problem in high dimensions. Current training algorithms are all based on gradient descent, which only guarantees convergence to a critical point (local minimum or ...

20

The two approaches provide very different guarantees. The JL Lemma says essentially "you give me the error you want, and I'll give you a low dimensional space that captures the distances upto that error". It's also a worst-case pairwise guarantee: for each pair of points, etc etc The SVD essentially promises "you tell me what dimension you want to live in,...

17

I presume you are talking about unconstrained minimization. Your question should specify if you are considering a specific problem structure. Otherwise, the answer is no. First I should dispel a myth. The classical gradient descent method (also called steepest descent method) is not even guaranteed to find a local minimizer. It stops when it has found a ...

17

Not sure how much of an answer this is, I'm just indulging in some rumination. Question 1 could be equally asked about P $\neq$ NP and with a similar answer -- the techniques/ideas used to prove the result would be the big breakthrough more so than the conclusion itself. For Question 2 I want to share some background and a thought. Pretty much all the ...

16

The question is, in my opinion, quite vague and involves some misunderstanding, so this answer attempts only to provide the right vocabulary and point you in the right direction. There are two fields of computer science that directly study such problems. Inductive inference and computational learning theory. The two fields are very closely related and the ...

14

As @TsuyoshiIto suggests, there is an $O(n\log n)$-time algorithm for this problem, due to Edelsbrunner and Preparata. In fact, their algorithm finds a convex polygon with the minimum possible number of edges that separates the two point sets. They also prove an $\Omega(n\log n)$ lower bound for the more general problem in the algebraic decision tree model;...

14

You ask why database aggregations have monoidal structure. Say we want to combine data values $a$ and $b$, but want to keep things general -- these may be integers, strings, floating point numbers, vectors, matrices, probability distributions, sets, or anything else we want to store and manipulate. So we denote the "aggregation" of $a$ and $b$ by $a.b$. ...

12

BP and most of its variants are proved to converge on graphs without cycles. When you have cycles they show very strange behavior sometimes. For these cases people have tried different approximations schemes, for example Sherali-Adams, Lovasz-Schrijver, and Lasserre Hierarchies. See  for a comprehensive review of these approximations. Also (Wainwright ...

11

I'll take a shot at your first question: Are there examples of natural function families that quantum computers can learn faster than classical computers given cryptographic assumptions? Well, it depends on the exact model and the resource being minimized. One option is to compare the sample complexity (for distribution-independent PAC learning) of the ...

11

Yes, some lower bounds are known. For example, assuming $NP \neq coNP$, you cannot even properly learn read-thrice DNF formulas in polynomial time. There is a whole paper developing such hardness results using something called the "representation problem". To answer your linked-to question: Schapire, in his dissertation, in addition to proving that "weak ...

11

There is a notion of property testing in which 1) is indeed "x is epsilon'-close to some y in L" (where epsilon' < epsilon), which is called tolerant property testing. In general, tolerant testers are harder to design than property tester (since property testing is a special case). In many cases, one can actually construct a one-sided error property ...

11

The success of a learning algorithm depends critically on the representation. How do you present the input to the algorithm? In an extreme case, suppose you present the numbers as sequences of prime factors -- in this case, learning is quite trivial. In another extreme, consider representing the numbers as binary strings. All the standard learning algorithms ...

11

If the learning algorithm is proper (i.e. it always produces a hypothesis from the class $F_n$), then it also gives a testing algorithm -- simply run the learning algorithm, and see whether the hypothesis it produced has error rate $<\epsilon$, which can be done with only $\approx 1/\epsilon^2$ samples. If it does, since the hypothesis is in $F_n$, this ...

11

Another take on this question, to add to @Aryeh's remarks: For many other models of learning, we know the "shape" of the hypothesis space. SVMs are the best example of this, in that what you're finding is a linear separator in a (possibly-high dimensional) Hilbert space. For neural networks in general, we don't have any such clear description or even an ...

11

I will formalize a variant of this question where "efficiency" is replaced by "computability". Let $C_n$ be the concept class of all languages $L\subseteq\Sigma^*$ recognizable by Turing machines on $n$ states or fewer. In general, for $x\in\Sigma^*$ and $f\in C_n$, the problem of evaluating $f(x)$ is undecidable. However, suppose we have access to a (...

10

There is no way to choose the parameters A, B, C, D properly; as it is the case for most heuristics, the parameters are chosen ''by experience''. Worse, there is no guarantee that the solution (the array of the outputs) of this heuristic is indeed a feasible TSP tour! In this context, perhaps interesting to read: Wilson and Pawley, On the stability of ...

10

PAC comes in two flavors -- "information theoretic PAC" and "efficient PAC." The latter asks for computational efficiency whereas the former cares only about sample size. One usually understands which is referred to from context. Indeed, it is not known whether (efficient) PAC learning is NP-hard in general, but results on the cryptographic hardness of ...

10

There are many open algorithmic problems. All problems below (other than the last bullet) are NP-hard, so we are interested in the best approximation ratio we can achieve in polynomial time. The following are just a sample: Given a non-negative submodular function on a universe $U$, find a set $A$ of size at most $k$ maximizing $f(A)$. The best known ...

10

In the classic PAC learning (i.e., classification) model, rare instances are not a problem. This is because the learner's test points are assumed to come from the same distribution as the training data. Thus, if a region of space is so sparse as to be poorly represented in the training sample, its probability of appearing during the test phase is low. You'...

9

We know something close to what you want. If you look at Ke Yang's "Honest Statistical Queries" -- there is no noise at all, but only "sampling error". In this model, you pass in a parameter $t$, and the Oracle takes $t$ samples, honestly evaluates the passed-in function (onto {0,1}), and returns the average value of the function on the samples. In ...

8

In my experience (YMMV) linear algebra is very much used as a tool in Machine Learning and Computer Vision, with little interest in the underlying mathematics. That said, you'll need a more mathematical understanding than, say, a games programmer or a basic researcher in the natural sciences. Basically you'll need to know enough to take a method like PCA ...

8

My solution is incorrect. See other answer...

8

Here is an easy argument: Assume there is a set $U$ of 5 points that can be shattered by balls. So for any set $S \subseteq U$, there exists a ball $B$ s.t. $B \cap U = S$ and a ball $B'$ s.t. $B' \cap U = U \setminus S$. Therefore, $B \cap B'$ contains no points of $U$. If $B \cap B' = \emptyset$, $B$ and $B'$ can be separated by a ...

8

What you describe is a non-stochastic version of the "functional multi-arm bandit problem": you know you have an unknown function from some class C (does not have to be randomly selected), and you have query access to this function. The goal is to find the element which maximizes the function. As you say, depending on the class C, this may or may not require ...

8

If your FSM is a DFA, then this is the Minimum Consistent DFA Problem, which is well known in the machine learning community. This problem is NP complete Gold1978 - Complexity of Automaton Identification from Given Data. The problem is also known to be hard to approximate within any polynomial factor. Indeed it is even hard to find an NFA whose number of ...

8

The approximate Caratheodory theorem goes back to the 60s, and probably way earlier than that (it follows for example from the mistake bound of the preceptron algorithm analysis). As for the dimensionality reduction, the answer is no - the supporting subsets are different subsets, and their number is too large. In particular, the number of possible subsets ...

8

My thanks to Aryeh for bringing this question to my attention. As others have mentioned, the answer to (1) is Yes, and the simple method of Empirical Risk Minimization in $\mathcal{C}$ achieves the $O((d/\varepsilon)\log(1/\varepsilon))$ sample complexity (see Vapnik and Chervonenkis, 1974; Blumer, Ehrenfeucht, Haussler, and Warmuth, 1989). As for (2), ...

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