Tag: st.statistics


Found 27 results for 'st.statistics'.


1) ds.algorithms - About learning a single Gaussian in total-variation distance
2) machine-learning - Is this a known learning problem?
3) pr.probability - Approximating distributions from samples
4) graph-theory - What is the connection between moments of Gaussians and perfect matchings of graphs?
5) machine-learning - Learning a discrete distribution in $\ell_r$ norm
6) machine-learning - Learning from derivative data
7) machine-learning - Learning a coin's bias (localized)
8) machine-learning - Tolerance parameter of statistical query model and adaptivity
9) quantum-computing - Distinguishing between $N$ quantum states
10) proofs - Proof Haar matrices satisfy JL lemma
11) ds.algorithms - Lower bound for testing closeness in $L_2$ norm?
12) pr.probability - Differential privacy definition: subset of range of values vs. equals a value in the range
13) machine-learning - What subjects, topics does a computer science graduate need to learn to apply available machine learning frameworks, esp. SVMs
14) ds.algorithms - Constraint Satisfaction Problem: Choosing real numbers with certain characteristics
15) it.information-theory - Notation of sequences in rate distortion theory
16) approximation-algorithms - complexity of fitting models to data
17) pr.probability - Binary search on coin heads probability
18) circuit-complexity - Circuit complexity and statistical tests
19) cc.complexity-theory - Theoretical guarantees for running times of belief propagation methods?
20) machine-learning - What happens if you minimize $D_{KL}(P_{parameters} || P_{data})$ under the Kullback-Leibler divergence?
21) machine-learning - Is there an equivalent to VC-dimension for density estimation as opposed to classification?
22) convex-optimization - Is the Chi-square divergence a Bregman divergence?
23) st.statistics - Design a sampling process to select an element with probability proportional to its appear probability in a simulation
24) it.information-theory - Why Asymptotic Equipartition Property theorem proofs assume the source is memoryless?
25) reference-request - Testing for finite expectation
26) lower-bounds - Sample complexity lower bound to learn the mode (the value with the highest probability) of a distribution with finite support
27) machine-learning - Upper bound for VCdim of $H$ in terms of subgraph$(F)$, where $H := \{S(f) | f \in F\}$, with $S(f) := \{(x,y) \in X \times \{\pm 1\} | yf(x) \le 1\}$