Search Results for author:
Found 17 papers, 3 papers with code
Statler: State-Maintaining Language Models for Embodied Reasoning
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework
In our paper, we introduce DP-HyPO, a pioneering framework for ``adaptive'' private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization.
Federated Linear Contextual Bandits with User-level Differential Privacy
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP).
Contraction of Locally Differentially Private Mechanisms
We investigate the contraction properties of locally differentially private mechanisms.
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition.
Robust Estimation for Random Graphs
We study the problem of robustly estimating the parameter $p$ of an Erd\H{o}s-R\'enyi random graph on $n$ nodes, where a $\gamma$ fraction of nodes may be adversarially corrupted.
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Finally, we prove nearly-matching lower bounds for private stochastic convex optimization with strongly convex losses and mean estimation, showing new separations between pure and concentrated DP.
Robust Testing and Estimation under Manipulation Attacks
We consider both the "centralized setting" and the "distributed setting with information constraints" including communication and local privacy (LDP) constraints.
Wide Network Learning with Differential Privacy
Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training regimes.
Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar via Manifold Optimization
The problem of joint design of transmit waveforms and receive filters is desirable in many application scenarios of multiple-input multiple-output (MIMO) radar systems.
Differentially Private Assouad, Fano, and Le Cam
The technical component of our paper relates coupling between distributions to the sample complexity of estimation under differential privacy.
Locally Private Hypothesis Selection
Absent privacy constraints, this problem requires $O(\log k)$ samples from $p$, and it was recently shown that the same complexity is achievable under (central) differential privacy.
Privately Learning Markov Random Fields
We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy.
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
In the second part of the paper, we extend our idea to the problem of estimating non-linear regressions and show similar results as in GLMs for both multivariate Gaussian and sub-Gaussian cases.
INSPECTRE: Privately Estimating the Unseen
We develop differentially private methods for estimating various distributional properties.
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication
All previously known sample optimal algorithms require linear (in $k$) communication from each user in the high privacy regime $(\varepsilon=O(1))$, and run in time that grows as $n\cdot k$, which can be prohibitive for large domain size $k$.
Differentially Private Testing of Identity and Closeness of Discrete Distributions
We propose a general framework to establish lower bounds on the sample complexity of statistical tasks under differential privacy.
