16 citations · 36 across the 16 of their papers we have counts for
16 papers
Efficient Opportunistic Approachability
Teodor Vanislavov Marinov, Mehryar Mohri, Princewill Okoroafor +2
We study the problem of opportunistic approachability: a generalization of Blackwell approachability where the learner would like to obtain stronger guarantees (i.e., approach a sm…
Design Considerations in Offline Preference-based RL
Alekh Agarwal, Christoph Dann, Teodor V. Marinov
Offline algorithms for Reinforcement Learning from Human Preferences (RLHF), which use only a fixed dataset of sampled responses given an input, and preference feedback among these…
Incentive-compatible Bandits: Importance Weighting No More
Julian Zimmert, Teodor V. Marinov
We study the problem of incentive-compatible online learning with bandit feedback. In this class of problems, the experts are self-interested agents who might misrepresent their pr…
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization
Teodor V. Marinov, Alekh Agarwal, Mircea Trofin
This work studies a Reinforcement Learning (RL) problem in which we are given a set of trajectories collected with K baseline policies. Each of these policies can be quite suboptim…
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov +1
We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples,…
Leveraging User-Triggered Supervision in Contextual Bandits
Alekh Agarwal, Claudio Gentile, Teodor V. Marinov
We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction…
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Teodor V. Marinov, Mehryar Mohri, Julian Zimmert
We revisit the problem of stochastic online learning with feedback graphs, with the goal of devising algorithms that are optimal, up to constants, both asymptotically and in finite…
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization
Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang
This paper considers the generalization performance of differentially private convex learning. We demonstrate that the convergence analysis of Langevin algorithms can be used to ob…
The Pareto Frontier of model selection for general Contextual Bandits
Teodor V. Marinov, Julian Zimmert
Recent progress in model selection raises the question of the fundamental limits of these techniques. Under specific scrutiny has been model selection for general contextual bandit…
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Christoph Dann, Teodor V. Marinov, Mehryar Mohri +1
We provide improved gap-dependent regret bounds for reinforcement learning in finite episodic Markov decision processes. Compared to prior work, our bounds depend on alternative de…
Corralling Stochastic Bandit Algorithms
Raman Arora, Teodor V. Marinov, Mehryar Mohri
We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a cor…
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
Raman Arora, Teodor V. Marinov, Enayat Ullah
In this paper, we revisit the problem of private stochastic convex optimization. We propose an algorithm based on noisy mirror descent, which achieves optimal rates both in terms o…
Bandits with Feedback Graphs and Switching Costs
Raman Arora, Teodor V. Marinov, Mehryar Mohri
We study the adversarial multi-armed bandit problem where partial observations are available and where, in addition to the loss incurred for each action, a \emph{switching cost} is…
Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor V. Marinov +1
The notion of \emph{policy regret} in online learning is a well defined? performance measure for the common scenario of adaptive adversaries, which more traditional quantities such…
Streaming Kernel PCA with Random Features
Enayat Ullah, Poorya Mianjy, Teodor V. Marinov +1
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, f…
Stochastic Approximation for Canonical Correlation Analysis
Raman Arora, Teodor V. Marinov, Poorya Mianjy +1
We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient…