Search Results for author:
Found 16 papers, 2 papers with code
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
Finally, we support our theory with illustrative experiments that compare the behavior of the average of the iterates with that of the last iterate in heavy-tailed noise regimes.
An Improved Uniform Convergence Bound with Fat-Shattering Dimension
The fat-shattering dimension characterizes the uniform convergence property of real-valued functions.
Active Learning of Classifiers with Label and Seed Queries
In this work we show that, by carefully combining the two types of queries, a binary classifier can be learned in time $\operatorname{poly}(n+m)$ using only $O(m^2 \log n)$ label queries and $O\big(m \log \frac{m}{\gamma}\big)$ seed queries; the result extends to $k$-class classifiers at the price of a $k! k^2$ multiplicative overhead.
Regret Analysis of Dyadic Search
We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
In this work we study high probability bounds for stochastic subgradient methods under heavy tailed noise.
A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization
This paper studies a natural generalization of the problem of minimizing a univariate convex function $f$ by querying its values sequentially.
On Margin-Based Cluster Recovery with Oracle Queries
We study an active cluster recovery problem where, given a set of $n$ points and an oracle answering queries like "are these two points in the same cluster?
Multitask Online Mirror Descent
We introduce and analyze MT-OMD, a multitask generalization of Online Mirror Descent (OMD) which operates by sharing updates between tasks.
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can be reconstructed exactly using only $O(\log n)$ same-cluster queries, where $n$ is the number of input points.
Robust Unsupervised Learning via L-Statistic Minimization
Designing learning algorithms that are resistant to perturbations of the underlying data distribution is a problem of wide practical and theoretical importance.
Exact Recovery of Mangled Clusters with Same-Cluster Queries
Given a finite set of input points, and an oracle revealing whether any two points lie in the same cluster, our goal is to recover all clusters exactly using as few queries as possible.
Correlation Clustering with Adaptive Similarity Queries
In this work we investigate correlation clustering as an active learning problem: each similarity score can be learned by making a query, and the goal is to minimise both the disagreements and the total number of queries.
Label Sanitization against Label Flipping Poisoning Attacks
Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points.
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack.
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
This exposes learning algorithms to the threat of data poisoning, i. e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process.
Efficient Attack Graph Analysis through Approximate Inference
We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages of approximate inference techniques to scale to larger attack graphs.
