TALK KEYWORD INDEX
This page contains an index consisting of author-provided keywords.
$ | |
$\ell_q$-based Laplacian regularization | |
1 | |
1-bit compressed sensing | |
A | |
Absolutely Minimal Lipschitz Extension | |
abstention | |
Active Learning | |
Adaptive data analysis | |
aggregation | |
approximation | |
Arithmetic Circuits | |
Asymptotic behavior | |
Auction Theory | |
B | |
bandit | |
Bandit problems | |
Bandit Theory | |
basis learning | |
bayesian | |
Bayesian algorithms | |
Belief propagation | |
best arm identification | |
best-arm identification | |
Best-case analysis | |
Blackwell Approachability | |
Burer-Monteiro heuristic | |
C | |
Calibration | |
classification | |
Collaborative filtering | |
collection of distributions | |
communication complexity | |
Community detection | |
Complexity of Learning | |
computational hardness | |
computational vs. statistical tradeoffs | |
concave objective | |
condensation threshold | |
constraint | |
contextual bandits | |
convex bandits | |
convex geometry | |
Cooperative Bandits | |
correlation decay | |
Cortical Computation | |
coverage functions | |
Curie-Weiss | |
D | |
Data Privacy | |
Deep Learning | |
degenerate saddle points | |
Delayed Feedback | |
Density evolution | |
depth hierarchy | |
Depth vs. Width | |
design of experiments | |
determinantal point processes sampling | |
dimension complexity | |
Distributed Learning | |
distribution learning | |
DNF | |
dueling bandits | |
E | |
ensembles | |
Error analysis | |
Exact Learning | |
exp-concave loss | |
Expert Advice | |
Exponential Weights | |
Expressive Power | |
F | |
Factored linear models | |
first-order methods | |
Fixed Confidence Setting | |
fluid model | |
Fourier analysis | |
Fourier transform | |
Function approximation | |
G | |
game theory | |
games | |
Generalization | |
generalization bounds | |
geodesic convexity | |
geometric concept classes | |
Geometric random graph model | |
Gradient descent | |
gradient iteration | |
H | |
Hardnes of learning | |
Hardness of approximation | |
hidden convexity | |
higher order conditions for local optimality | |
I | |
image denoising | |
Improper learning | |
information processing system | |
Interactive algorithms | |
Ising models | |
isotonic regression | |
iteration complexity | |
Iterative Constructions | |
J | |
Juntas | |
K | |
kernel machines | |
Knapsack constraints | |
L | |
Lasserre hierarchy | |
Lasso path | |
Latent Variable Model | |
learning | |
learning combinatorial functions | |
Learning distributions | |
learning halfspaces | |
learning parities | |
Learning Theory | |
learning with noise | |
Linear regression | |
linear sets | |
local minimum | |
Low Rank | |
Low-rank matrix completion | |
Lower Bounds | |
LUCB | |
M | |
manifold optimization | |
Manipulation-resistance | |
markov chains | |
Markov Decision Process | |
matrix concentration | |
matroid | |
maximum classes | |
MDL | |
Method of Moments | |
metric entropy | |
minimax | |
mixing time | |
model selection | |
Model-based reinforcement learning | |
modeling errors | |
Modular Square Root Extraction | |
Monotone functions | |
monotonic regression | |
Monte Carlo markov chain algorithms | |
Multi-arm Arm Bandits | |
multi-armed bandit | |
multi-armed bandit problems | |
multi-armed bandits | |
Multi-label learning | |
Multiparameter Mechanism Design | |
N | |
Neural networks | |
noisy power method | |
non-convex | |
Non-convex optimization | |
non-mixing | |
non-stationary | |
nonparametric regression | |
nonpositively curved spaces | |
Nonstochastic Bandits | |
O | |
on-line learning | |
online | |
online learning | |
online sparse regression | |
optimization | |
optimization over polynomials with real roots | |
oracle inequalities | |
P | |
pairwise comparisons | |
Partially Observable Markov Decision Process | |
partition function | |
phase transition | |
Planted dense subgraph recovery | |
planted partitioning | |
planted satisfiability | |
Poisson binomial distribution | |
pool-based | |
power methods | |
prediction loss | |
preference relation | |
principal component analysis | |
proper learning | |
Property testing | |
pure exploration | |
R | |
racing | |
random matrices | |
rank minimization | |
Ranking | |
recursive teaching dimension | |
refutation | |
regret | |
regret analysis | |
regret bounds | |
Regret minimization | |
reinforcement learning | |
Repeated auctions | |
representation | |
Reputation systems | |
resource-constrained learning | |
robust algorithms | |
Robustness | |
S | |
saddle points | |
SDPLR | |
Second price auctions | |
semi-definite | |
semi-definite programming | |
Semi-supervised learning | |
Semidefinite Programming | |
Semidefinite programming relaxations | |
sequential hypothesis testing | |
SGD | |
sharp oracle inequalities | |
Sherali-Adams hierarchy | |
sign rank | |
Simple Regret | |
Smoothness | |
Sparse PCA | |
spectral algorithms | |
spectral gap | |
Spectral Methods | |
stability | |
statistical query model | |
stochastic and adversarial | |
stochastic block model | |
stochastic block models | |
stochastic gradient descent | |
stochastic resource allocation | |
stream-based | |
streaming algorithms | |
strongly Rayleigh distributions | |
Submatrix localization | |
submodularity | |
sum-of-squares | |
sums of independent random variables | |
Synchronization | |
T | |
teaching dimension | |
tensor completion | |
Tensor Decompositions | |
thompson sampling | |
threshold | |
Threshold functions | |
time series prediction | |
top eigenvector | |
total variation regularization | |
trust region methods | |
V | |
validation | |
variational inference | |
variational methods | |
VC dimension | |
Vickrey auctions |