28 citations · 127 across the 46 of their papers we have counts for
13 papers · 1 filter
Learning-Enabled Estimation: Tight Characterizations under Sample Selection Biases
Vikram Kher, Jane H. Lee, Anay Mehrotra +1
When can we learn from biased samples? We study regression when outcomes are observed only after passing through selection filters that depend on both covariates and outcomes thems…
Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis +2
Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measu…
Private Statistical Estimation via Truncation
Manolis Zampetakis, Felix Zhou
We introduce a novel framework for differentially private (DP) statistical estimation via data truncation, addressing a key challenge in DP estimation when the data support is unbo…
On the Hardness of Learning One Hidden Layer Neural Networks
Shuchen Li, Ilias Zadik, Manolis Zampetakis
In this work, we consider the problem of learning one hidden layer ReLU neural networks with inputs from . We show that this learning problem is hard under standard c…
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
Alkis Kalavasis, Amin Karbasi, Argyris Oikonomou +3
As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks.…
Transfer Learning Beyond Bounded Density Ratios
Alkis Kalavasis, Ilias Zadik, Manolis Zampetakis
We study the fundamental problem of transfer learning where a learning algorithm collects data from some source distribution but needs to perform well with respect to a differe…
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik +4
While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designe…
Deterministic Nonsmooth Nonconvex Optimization
Michael I. Jordan, Guy Kornowski, Tianyi Lin +2
We study the complexity of optimizing nonsmooth nonconvex Lipschitz functions by producing -stationary points. Several recent works have presented randomized algorithms that…
Learning and Covering Sums of Independent Random Variables with Unbounded Support
Alkis Kalavasis, Konstantinos Stavropoulos, Manolis Zampetakis
We study the problem of covering and learning sums of independent integer-valued random variables (SIIRVs) with unbounded, or even infinite, support.…
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis +1
Min-max optimization problems involving nonconvex-nonconcave objectives have found important applications in adversarial training and other multi-agent learning settings. Yet, no k…
Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni +1
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensi…
Truncated Linear Regression in High Dimensions
Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis
As in standard linear regression, in truncated linear regression, we are given access to observations whose dependent variable equals $y_i= A_i^{\rm T} \cdot x^* + η…
Optimal Learning of Mallows Block Model
Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi +1
The Mallows model, introduced in the seminal paper of Mallows 1957, is one of the most fundamental ranking distribution over the symmetric group . To analyze more complex rank…