Search Results for author:
Found 34 papers, 7 papers with code
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
The best existing efficient (i. e., polynomial-time) algorithms for this problem only guarantee a $O(T^{2/3})$ upper-bound on the regret.
Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
In this paper, we designed efficient algorithms for the problem of regret minimization in assortment selection with \emph{Plackett Luce} (PL) based user choices.
Covariance-Adaptive Least-Squares Algorithm for Stochastic Combinatorial Semi-Bandits
We address the problem of stochastic combinatorial semi-bandits, where a player can select from P subsets of a set containing d base items.
Online Learning Approach for Survival Analysis
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data.
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them.
Adaptive approximation of monotone functions
We prove that GreedyBox achieves an optimal sample complexity for any function $f$, up to logarithmic factors.
Sequential Counterfactual Risk Minimization
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
Reimagining Demand-Side Management with Mean Field Learning
Integrating renewable energy into the power grid while balancing supply and demand is a complex issue, given its intermittent nature.
One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
We address the problem of \emph{`Internal Regret'} in \emph{Sleeping Bandits} in the fully adversarial setup, as well as draw connections between different existing notions of sleeping regrets in the multiarmed bandits (MAB) literature and consequently analyze the implications: Our first contribution is to propose the new notion of \emph{Internal Regret} for sleeping MAB.
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences
We study the problem of $K$-armed dueling bandit for both stochastic and adversarial environments, where the goal of the learner is to aggregate information through relative preferences of pair of decisions points queried in an online sequential manner.
Efficient Kernel UCB for Contextual Bandits
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
We introduce the ``continuized'' Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions.
Mixability made efficient: Fast online multiclass logistic regression
Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret.
Dueling Bandits with Adversarial Sleeping
The goal is to find an optimal `no-regret' policy that can identify the best available item at each round, as opposed to the standard `fixed best-arm regret objective' of dueling bandits.
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.
A Continuized View on Nesterov Acceleration
We introduce the "continuized" Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.
Distributed, Parallel, and Cluster Computing Optimization and Control
Online nonparametric regression with Sobolev kernels
In this work we investigate the variation of the online kernelized ridge regression algorithm in the setting of $d-$dimensional adversarial nonparametric regression.
Non-stationary Online Regression
To the best of our knowledge, this work is the first extension of non-stationary online regression to non-stationary kernel regression.
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
In the context of statistical supervised learning, the noiseless linear model assumes that there exists a deterministic linear relation $Y = \langle \theta_*, X \rangle$ between the random output $Y$ and the random feature vector $\Phi(U)$, a potentially non-linear transformation of the inputs $U$.
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards
We then study the most general version of the problem where at each round available sets are generated from some unknown arbitrary distribution (i. e., without the independence assumption) and propose an efficient algorithm with $O(\sqrt {2^K T})$ regret guarantee.
Efficient improper learning for online logistic regression
We consider the setting of online logistic regression and consider the regret with respect to the 2-ball of radius B.
Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index
In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen's additive regression model), parametric (Weibull AFT model), and machine learning models (Random Survival Forest, Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through the concordance index on two different datasets (PBC and GBCSG2).
Efficient online learning with kernels for adversarial large scale problems
For $d$-dimensional inputs, we provide a (close to) optimal regret of order $O((\log n)^{d+1})$ with per-round time complexity and space complexity $O((\log n)^{2d})$.
Target Tracking for Contextual Bandits: Application to Demand Side Management
We propose a contextual-bandit approach for demand side management by offering price incentives.
Uniform regret bounds over $R^d$ for the sequential linear regression problem with the square loss
In the case of sequentially revealed features, we also derive an asymptotic regret bound of $d B^2 \ln T$ for any individual sequence of features and bounded observations.
Efficient online algorithms for fast-rate regret bounds under sparsity
setting, we establish new risk bounds that are adaptive to the sparsity of the problem and to the regularity of the risk (ranging from a rate 1 / $\sqrt T$ for general convex risk to 1 /T for strongly convex risk).
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations
We develop a method solving the gossip problem that depends only on the spectral dimension of the network, that is, in the communication network set-up, the dimension of the space in which the agents live.
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information.
A Chaining Algorithm for Online Nonparametric Regression
We consider the problem of online nonparametric regression with arbitrary deterministic sequences.
A consistent deterministic regression tree for non-parametric prediction of time series
We study online prediction of bounded stationary ergodic processes.
A Second-order Bound with Excess Losses
We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algorithm (and also a version of the polynomially weighted average algorithm) with multiple learning rates.
Mirror Descent Meets Fixed Share (and feels no regret)
Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension.
