The following pages link to A theory of the learnable (Q3714486):
Displaying 50 items.
- Multiple-source adaptation theory and algorithms (Q2035631) (← links)
- Adaptively secure distributed PRFs from \(\mathsf{LWE}\) (Q2043324) (← links)
- Classifier-based constraint acquisition (Q2043441) (← links)
- Top program construction and reduction for polynomial time meta-interpretive learning (Q2051277) (← links)
- New cryptographic hardness for learning intersections of halfspaces over Boolean cubes with membership queries (Q2051795) (← links)
- Approximate minimization of weighted tree automata (Q2064525) (← links)
- Learning algorithms (Q2074213) (← links)
- Quantum learning of concentrated Boolean functions (Q2101535) (← links)
- Analyzing and repairing concept drift adaptation in data stream classification (Q2102402) (← links)
- A quantum algorithm of K-means toward practical use (Q2107106) (← links)
- Conformal prediction: a unified review of theory and new challenges (Q2108468) (← links)
- Distribution-free robust linear regression (Q2113267) (← links)
- Understanding generalization error of SGD in nonconvex optimization (Q2127232) (← links)
- Testing graphs against an unknown distribution (Q2130521) (← links)
- RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (Q2138017) (← links)
- Low-complexity weak pseudorandom functions in \(\mathtt{AC}0[\mathtt{MOD}2]\) (Q2139645) (← links)
- Learning finite state models from recurrent neural networks (Q2165516) (← links)
- Off-line reasoning for on-line efficiency: knowledge bases (Q2171259) (← links)
- PALO: a probabilistic hill-climbing algorithm (Q2171267) (← links)
- Algebraic machine learning: emphasis on efficiency (Q2171721) (← links)
- Minimal consistent DFA from sample strings (Q2182675) (← links)
- Learning under \(p\)-tampering poisoning attacks (Q2202514) (← links)
- On biased random walks, corrupted intervals, and learning under adversarial design (Q2202524) (← links)
- PCPs and the hardness of generating synthetic data (Q2210447) (← links)
- Learning with mitigating random consistency from the accuracy measure (Q2217414) (← links)
- Fast greedy \(\mathcal{C} \)-bound minimization with guarantees (Q2217455) (← links)
- Classification with label noise: a Markov chain sampling framework (Q2218376) (← links)
- Learning privately with labeled and unlabeled examples (Q2223696) (← links)
- Discriminative training of conditional random fields with probably submodular constraints (Q2226189) (← links)
- Making sense of raw input (Q2238697) (← links)
- Best lower bound on the probability of a binomial exceeding its expectation (Q2244539) (← links)
- Bounds on the sample complexity for private learning and private data release (Q2251471) (← links)
- PAC-learning in the presence of one-sided classification~noise (Q2254605) (← links)
- Robust domain adaptation (Q2254608) (← links)
- Getting CICY high (Q2273830) (← links)
- The complexity of exact learning of acyclic conditional preference networks from swap examples (Q2289025) (← links)
- The power of random counterexamples (Q2290678) (← links)
- Tight bounds on \(\ell_1\) approximation and learning of self-bounding functions (Q2290687) (← links)
- A linear relation between input and first layer in neural networks (Q2294577) (← links)
- Deep learning the hyperbolic volume of a knot (Q2294618) (← links)
- Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
- On PAC-Bayesian bounds for random forests (Q2320582) (← links)
- The teaching size: computable teachers and learners for universal languages (Q2320594) (← links)
- Probably bounded suboptimal heuristic search (Q2321255) (← links)
- Proper learning of \(k\)-term DNF formulas from satisfying assignments (Q2323349) (← links)
- Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model (Q2328061) (← links)
- Interpolation, the rudimentary geometry of spaces of Lipschitz functions, and geometric complexity (Q2329040) (← links)
- Mean estimation and regression under heavy-tailed distributions: A survey (Q2329044) (← links)
- Statistical active learning algorithms for noise tolerance and differential privacy (Q2345952) (← links)
- Complexity measures and concept learning (Q2348075) (← links)
