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Found 18 papers, 3 papers with code
AlphaPO -- Reward shape matters for LLM alignment
In this paper, we argue that, for DAAs the reward (function) shape matters.
Logit Attenuating Weight Normalization
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.
Efficient Vertex-Oriented Polytopic Projection for Web-scale Applications
We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes.
Regression via Implicit Models and Optimal Transport Cost Minimization
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x, z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$.
Targeted display advertising: the case of preferential attachment
We present an experimental analysis on the historical logs of a major display advertising platform (https://www. criteo. com/).
Benchmarking Regression Methods: A comparison with CGAN
Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input.
Learning State Representations for Query Optimization with Deep Reinforcement Learning
In the database field, query optimization remains a difficult problem.
Distributed Newton Methods for Deep Neural Networks
First, to reduce the communication cost, we propose a diagonalization method such that an approximate Newton direction can be obtained without communication between machines.
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
We also demonstrate their usefulness in making design choices such as the number of classifiers in the ensemble and the size of a subset of data used for training that is needed to achieve a certain value of generalization error.
Gradient Boosted Decision Trees for High Dimensional Sparse Output
In this paper, we study the gradient boosted decision trees (GBDT) when the output space is high dimensional and sparse.
Batch-Expansion Training: An Efficient Optimization Framework
We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset.
Towards a Better Understanding of Predict and Count Models
In a recent paper, Levy and Goldberg pointed out an interesting connection between prediction-based word embedding models and count models based on pointwise mutual information.
A Distributed Algorithm for Training Nonlinear Kernel Machines
This paper concerns the distributed training of nonlinear kernel machines on Map-Reduce.
A distributed block coordinate descent method for training $l_1$ regularized linear classifiers
In this paper we design a distributed algorithm for $l_1$ regularization that is much better suited for such systems than existing algorithms.
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
We consider the problem of quantitatively evaluating missing value imputation algorithms.
A Structured Prediction Approach for Missing Value Imputation
In this paper we propose a structured output approach for missing value imputation that also incorporates domain constraints.
An efficient distributed learning algorithm based on effective local functional approximations
In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs.
