56 research outputs found
Preserving Randomness for Adaptive Algorithms
Suppose Est is a randomized estimation algorithm that uses n random bits and outputs values in R^d. We show how to execute Est on k adaptively chosen inputs using only n + O(k log(d + 1)) random bits instead of the trivial nk (at the cost of mild increases in the error and failure probability). Our algorithm combines a variant of the INW pseudorandom generator [Impagliazzo et al., 1994] with a new scheme for shifting and rounding the outputs of Est. We prove that modifying the outputs of Est is necessary in this setting, and furthermore, our algorithm\u27s randomness complexity is near-optimal in the case d {-1, 1} using O(n log n) * poly(1/theta) queries to F and O(n) random bits (independent of theta), improving previous work by Bshouty et al. [Bshouty et al., 2004]
A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity
A remarkable recent paper by Rubinfeld and Vasilyan (2022) initiated the
study of \emph{testable learning}, where the goal is to replace hard-to-verify
distributional assumptions (such as Gaussianity) with efficiently testable ones
and to require that the learner succeed whenever the unknown distribution
passes the corresponding test. In this model, they gave an efficient algorithm
for learning halfspaces under testable assumptions that are provably satisfied
by Gaussians.
In this paper we give a powerful new approach for developing algorithms for
testable learning using tools from moment matching and metric distances in
probability. We obtain efficient testable learners for any concept class that
admits low-degree \emph{sandwiching polynomials}, capturing most important
examples for which we have ordinary agnostic learners. We recover the results
of Rubinfeld and Vasilyan as a corollary of our techniques while achieving
improved, near-optimal sample complexity bounds for a broad range of concept
classes and distributions.
Surprisingly, we show that the information-theoretic sample complexity of
testable learning is tightly characterized by the Rademacher complexity of the
concept class, one of the most well-studied measures in statistical learning
theory. In particular, uniform convergence is necessary and sufficient for
testable learning. This leads to a fundamental separation from (ordinary)
distribution-specific agnostic learning, where uniform convergence is
sufficient but not necessary.Comment: 34 page
An Efficient Tester-Learner for Halfspaces
We give the first efficient algorithm for learning halfspaces in the testable
learning model recently defined by Rubinfeld and Vasilyan (2023). In this
model, a learner certifies that the accuracy of its output hypothesis is near
optimal whenever the training set passes an associated test, and training sets
drawn from some target distribution -- e.g., the Gaussian -- must pass the
test. This model is more challenging than distribution-specific agnostic or
Massart noise models where the learner is allowed to fail arbitrarily if the
distributional assumption does not hold.
We consider the setting where the target distribution is Gaussian (or more
generally any strongly log-concave distribution) in dimensions and the
noise model is either Massart or adversarial (agnostic). For Massart noise, our
tester-learner runs in polynomial time and outputs a hypothesis with
(information-theoretically optimal) error for any
strongly log-concave target distribution. For adversarial noise, our
tester-learner obtains error in polynomial time
when the target distribution is Gaussian; for strongly log-concave
distributions, we obtain in
quasipolynomial time.
Prior work on testable learning ignores the labels in the training set and
checks that the empirical moments of the covariates are close to the moments of
the base distribution. Here we develop new tests of independent interest that
make critical use of the labels and combine them with the moment-matching
approach of Gollakota et al. (2023). This enables us to simulate a variant of
the algorithm of Diakonikolas et al. (2020) for learning noisy halfspaces using
nonconvex SGD but in the testable learning setting.Comment: 26 pages, 3 figures, Version v2: strengthened the agnostic guarante
List-decoding reed-muller codes over small fields
We present the first local list-decoding algorithm for the rth order Reed-Muller code RM(r,m) over F2 for r ≥ 2. Given an oracle for a received word R: Fm2 → F2, our random-ized local list-decoding algorithm produces a list containing all degree r polynomials within relative distance (2−r − ε) from R for any ε> 0 in time poly(mr, ε−r). The list size could be exponential in m at radius 2−r, so our bound is op-timal in the local setting. Since RM(r,m) has relative dis-tance 2−r, our algorithm beats the Johnson bound for r ≥ 2. In the setting where we are allowed running-time polyno-mial in the block-length, we show that list-decoding is pos-sible up to even larger radii, beyond the minimum distance. We give a deterministic list-decoder that works at error rate below J(21−r), where J(δ) denotes the Johnson radius for minimum distance δ. This shows that RM(2,m) codes are list-decodable up to radius η for any constant η < 1 2 in time polynomial in the block-length. Over small fields Fq, we present list-decoding algorithms in both the global and local settings that work up to the list-decoding radius. We conjecture that the list-decoding radius approaches the minimum distance (like over F2), and prove this holds true when the degree is divisible by q − 1
Predicting a Protein's Stability under a Million Mutations
Stabilizing proteins is a foundational step in protein engineering. However,
the evolutionary pressure of all extant proteins makes identifying the scarce
number of mutations that will improve thermodynamic stability challenging. Deep
learning has recently emerged as a powerful tool for identifying promising
mutations. Existing approaches, however, are computationally expensive, as the
number of model inferences scales with the number of mutations queried. Our
main contribution is a simple, parallel decoding algorithm. Our Mutate
Everything is capable of predicting the effect of all single and double
mutations in one forward pass. It is even versatile enough to predict
higher-order mutations with minimal computational overhead. We build Mutate
Everything on top of ESM2 and AlphaFold, neither of which were trained to
predict thermodynamic stability. We trained on the Mega-Scale cDNA proteolysis
dataset and achieved state-of-the-art performance on single and higher-order
mutations on S669, ProTherm, and ProteinGym datasets. Code is available at
https://github.com/jozhang97/MutateEverythingComment: NeurIPS 2023. Code available at
https://github.com/jozhang97/MutateEverythin
A complexity theoretic approach to learning
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.Includes bibliographical references (leaves 127-138).This thesis details a new vantage point for attacking longstanding problems in machine learning. We use tools from computational complexity theory to make progress on problems from computational learning theory. Our methods yield the fastest and most expressive algorithms to date for learning several fundamental concept classes: * We show that any s-term DNF over n variables can be computed by a polynomial threshold function of order O(n1/3 log s). As an immediate consequence we obtain the fastest known DNF learning algorithm which runs in time 2O(n1/3). * We give the first polynomial time algorithm to learn an intersection of a constant number of halfspaces under the uniform distribution to within any constant error parameter. We also give the first quasipolynomial time algorithm for learning any function of a constant number of halfspaces with polynomial bounded weights under any distribution. * We give an algorithm to learn constant-depth polynomial-size circuits augmented with majority gates under the uniform distribution using random examples only. For circuits which contain a polylogarithmic number of majority gates the algorithm runs in quasipolynomial time. Under a suitable cryptographic assumption we show that these are the most expressive circuits which will admit a non-trivial learning algorithm. Our approach relies heavily on giving novel representations of well known concept classes via complexity theoretic reductions. We exploit the fact that many results in computational learning theory have a complexity theoretic analogue or implication. As such,(cont.) we also obtain new results in computational complexity including (1) a proof that the 30 year old lower bound due to Minsky and Papert [88] on the degree of a perceptron computing a DNF formula is tight and (2) improved constructions of pseudo-random generators, mathematical objects which play a fundamental role in cryptography and derandomization.by Adam Richard Klivans.Ph.D
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