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Found 37 papers, 0 papers with code
Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Losses
This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions.
A Note On Nonlinear Regression Under L2 Loss
We investigate the nonlinear regression problem under L2 loss (square loss) functions.
Efficient Lipschitzian Global Optimization of Hölder Continuous Multivariate Functions
This study presents an effective global optimization technique designed for multivariate functions that are H\"older continuous.
Formulation of Weighted Average Smoothing as a Projection of the Origin onto a Convex Polytope
Our study focuses on determining the best weight windows for a weighted moving average smoother under squared loss.
A 2-opt Algorithm for Locally Optimal Set Partition Optimization
Our research deals with the optimization version of the set partition problem, where the objective is to minimize the absolute difference between the sums of the two disjoint partitions.
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback
Our algorithm works from a universal prediction perspective and the performance measure used is the expected regret against arbitrary comparator sequences, which is the difference between our losses and a competing loss sequence.
$1D$ to $nD$: A Meta Algorithm for Multivariate Global Optimization via Univariate Optimizers
In this work, we propose a meta algorithm that can solve a multivariate global optimization problem using univariate global optimizers.
Optimal Tracking in Prediction with Expert Advice
We study the prediction with expert advice setting, where the aim is to produce a decision by combining the decisions generated by a set of experts, e. g., independently running algorithms.
An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows
We investigate an auto-regressive formulation for the problem of smoothing time-series by manipulating the inherent objective function of the traditional moving mean smoothers.
Efficient Minimax Optimal Global Optimization of Lipschitz Continuous Multivariate Functions
In this work, we propose an efficient minimax optimal global optimization algorithm for multivariate Lipschitz continuous functions.
A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression
We study the sequential calibration of estimations in a quantized isotonic L2 regression setting.
Robust, Nonparametric, Efficient Decomposition of Spectral Peaks under Distortion and Interference
We propose a decomposition method for the spectral peaks in an observed frequency spectrum, which is efficiently acquired by utilizing the Fast Fourier Transform.
Second Order Regret Bounds Against Generalized Expert Sequences under Partial Bandit Feedback
We study the problem of expert advice under partial bandit feedback setting and create a sequential minimax optimal algorithm.
Blind Source Separation for Mixture of Sinusoids with Near-Linear Computational Complexity
We propose a multi-tone decomposition algorithm that can find the frequencies, amplitudes and phases of the fundamental sinusoids in a noisy observation sequence.
Merging Knockout and Round-Robin Tournaments: A Flexible Linear Elimination Tournament Design
We propose a new tournament structure that combines the popular knockout tournaments and the round-robin tournaments.
Natural Hierarchical Cluster Analysis by Nearest Neighbors with Near-Linear Time Complexity
We propose a nearest neighbor based clustering algorithm that results in a naturally defined hierarchy of clusters.
A Linearithmic Time Locally Optimal Algorithm for the Multiway Number Partition Optimization
We study the problem of multiway number partition optimization, which has a myriad of applications in the decision, learning and optimization literature.
Smoothing with the Best Rectangle Window is Optimal for All Tapered Rectangle Windows
We show that the best rectangle window is optimal for such window definitions.
Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics
We propose an extended generalization of the pseudo Huber loss formulation.
Low Regret Binary Sampling Method for Efficient Global Optimization of Univariate Functions
For a search space of $[0, 1]$, our approach has at most $L\log (3T)$ and $2. 25H$ regret for $L$-Lipschitz continuous and $H$-Lipschitz smooth functions respectively.
Efficient, Anytime Algorithms for Calibration with Isotonic Regression under Strictly Convex Losses
We start by studying the traditional square error setting with its weighted variant and show that the optimal monotone transform is in the form of a unique staircase function.
Near-Linear Time Algorithm with Near-Logarithmic Regret Per Switch for Mixable/Exp-Concave Losses
We investigate the problem of online learning, which has gained significant attention in recent years due to its applicability in a wide range of fields from machine learning to game theory.
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits
We study the adversarial multi-armed bandit problem and create a completely online algorithmic framework that is invariant under arbitrary translations and scales of the arm losses.
A Quadratic Time Locally Optimal Algorithm for NP-hard Equal Cardinality Partition Optimization
We study the optimization version of the equal cardinality set partition problem (where the absolute difference between the equal sized partitions' sums are minimized).
Efficient Locally Optimal Number Set Partitioning for Scheduling, Allocation and Fair Selection
We study the optimization version of the set partition problem (where the difference between the partition sums are minimized), which has numerous applications in decision theory literature.
Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering
In this paper, we propose a nonparametric approach that can be used in envelope extraction, peak-burst detection and clustering in time series.
Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics
We propose a generalized formulation of the Huber loss.
Cumulative Regret Analysis of the Piyavskii--Shubert Algorithm and Its Variants for Global Optimization
For $L$-Lipschitz continuous functions, we show that the cumulative regret is $O(L\log T)$.
Optimally Efficient Sequential Calibration of Binary Classifiers to Minimize Classification Error
In this work, we aim to calibrate the score outputs of an estimator for the binary classification problem by finding an 'optimal' mapping to class probabilities, where the 'optimal' mapping is in the sense that minimizes the classification error (or equivalently, maximizes the accuracy).
Optimal and Efficient Algorithms for General Mixable Losses against Switching Oracles
The best dynamic estimation sequence that we compete against is selected in hindsight with full observation of the loss functions and is allowed to select different optimal estimations in different time intervals (segments).
Recursive Experts: An Efficient Optimal Mixture of Learning Systems in Dynamic Environments
By exploiting this relation, specific learning systems can be designed that perform asymptotically optimal for various applications.
A Generalized Online Algorithm for Translation and Scale Invariant Prediction with Expert Advice
In this work, we aim to create a completely online algorithmic framework for prediction with expert advice that is translation-free and scale-free of the expert losses.
Minimax Optimal Algorithms for Adversarial Bandit Problem with Multiple Plays
To construct our algorithm, we introduce a new expert advice algorithm for the multiple-play setting.
Universal Online Convex Optimization with Minimax Optimal Second-Order Dynamic Regret
Our approach can compete against all comparator sequences simultaneously (universally) in a minimax optimal manner, i. e. each regret guarantee depends on the respective comparator path variation.
Accelerating Min-Max Optimization with Application to Minimal Bounding Sphere
We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain.
Minimax Optimal Online Stochastic Learning for Sequences of Convex Functions under Sub-Gradient Observation Failures
We specifically study scenarios where our sub-gradient observations can be noisy or even completely missing in a stochastic manner.
An Asymptotically Optimal Algorithm for Communicating Multiplayer Multi-Armed Bandit Problems
Players are not aware about the probability distributions of the arms.
