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The Optimal Sample Complexity of PAC Learning
... the learning theory literature, we adopt the assumption that the events appearing in probability claims below are indeed ...for sample- consistent classifiers (Lemma 4 ... See full document
15
Refined Error Bounds for Several Learning Algorithms
... of learning linear separators under a uniform distribu- tion by Balcan, Broder, and Zhang (2007) (see also Dasgupta, Kalai, and Monteleoni, 2005, for related ...consistent learning algorithm achieves the ... See full document
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The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
... We consider the multi-armed bandit problem under the PAC (“probably approximately correct”) model. It was shown by Even-Dar et al. (2002) that given n arms, a total of O (n/ε 2 )log(1/δ) trials suffices in order ... See full document
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An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity
... the learning to rank (LTR) ...machine learning practitioners ...of learning to rank items, but wish to avoid obtaining labels for the quadratically many preference pairs, without compromising low ... See full document
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On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
... conjectured optimal exploration rate log((log(t) + 1)/δ), almost provably δ-PAC according to Theorem 8 (bold green symbols), and the rate log(1/δ), which would be appropriate if we were to perform the ... See full document
42
The Sample Complexity of Dictionary Learning
... The Babel function is one of several metrics defined in the sparse representations literature to quan- tify an ”almost orthogonality” property that dictionaries may enjoy. Such properties have been shown to imply ... See full document
23
Smooth Boosting and Learning with Malicious Noise
... -based PAC-model p-norm algorithms of Servedio (2000) is that they do not appear to succeed in the presence of malicious ...new PAC algorithms which use SmoothBoost match both the sample ... See full document
16
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
... Private learning. Private learning was introduced in [22] as a combination of Valiant’s PAC learning model [29] and differential ...private learning has mainly focused on pure ...using ... See full document
61
Reinforcement Learning in Finite MDPs: PAC Analysis
... 1998). Sample complexity analysis not only shows soundness of this principle in a mathematically precise manner, but also motivates novel RL algorithms with efficient exploration ...in sample ... See full document
32
On the Sample Complexity of Reinforcement Learning
... a sample based counterpart to the exact dynamic programming ...tightened sample complexity bound (as compared to Keams and Singh [1999]) is provided on how many samples are required in order for the ... See full document
143
The Sample Complexity of Learning Linear Predictors with the Squared Loss
... online learning, it implies that the Vovk-Azoury- Warmuth forecaster is essentially optimal in our setting and for a finite-dimensional regime, in terms of its dependence on both d and B (the lower bounds ... See full document
12
Optimal Quantum Sample Complexity of Learning Algorithms
... quantum learning, referring to our survey Arunacha- lam and de Wolf (2017) for more ...on sample complexity, which is a fundamental information-theoretic ...quantity. Sample complexity ... See full document
36
Order-Revealing Encryption and the Hardness of Private Learning
... private learning established that, ignoring computational complex- ity, any concept class is privately learnable with a number of samples logarithmic in the size of the concept class [KLN + ...the sample ... See full document
29
Learning Factor Graphs in Polynomial Time and Sample Complexity
... inference is tractable, there have been two main research tracks for ML structure learning. The first, starting with the work of Della Pietra et al. (1997), uses local-search heuristics to add factors into the ... See full document
46
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers
... single sample-compressed classifier, the general risk bound reduces to a bound similar to the tight sample-compression bound of Laviolette et ...The PAC-Bayes risk bound of Theorem 3 is, however, ... See full document
27
Local Identification of Overcomplete Dictionaries
... finite sample size recovery results with high probability as long as the sample size N scales as O(K 3 dS ε ˜ −2 ), where the recovery precision ˜ ε can go down to the asymptotically achievable ...has ... See full document
32
Lifelong Reinforcement Learning On Mobile Robots
... To demonstrate our method on a real Turtlebot, four tasks were learned using conven- tional PG and then transfer was evaluated on the fifth task. The number of roll-outs is reduced to n = 11 and the number of ... See full document
197
Characterizing the Sample Complexity of Pure Private Learners
... private learning algorithm with sample complexity O(1), matching a dif- ferent algorithm of Beimel et ...the sample complexity compared to the algorithm of Beimel et ...the ... See full document
33
Running with the PAC
... who deliberately went long periods without tagging a pill despite losing a point for every second they went without tagging a pill. This occurred on a couple of occasions which did not seem to prolong the game much above ... See full document
8
Concurrent Secure Computation with Optimal Query Complexity
... Our approach in a nutshell. Towards that end, our starting observation is that the problem of bounding the simulator query complexity per session can be reduced to bounding the number of times the output message ... See full document
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