A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.
The paper develops a theory for speculative decoding acceptance criteria.
Top-H decoding improves text generation by balancing creativity and coherence.
Regularization-induced exploration improves contextual bandit performance.
Recurrent models can produce infinite sequences, causing bias; new methods prevent this.
New method shows multi-objective bandits are not harder than single-objective ones.
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the action-values, with the addition of entropy regularization for soft variants. In th…
A new energy-efficient pruning method for federated learning.
Robust subgroup discovery finds non-redundant, statistically significant subgroups.
Archaeologists are in dire need of automated object reconstruction methods. Fragments reassembly is close to puzzle problems, which may be solved by computer vision algorithms. As they are often beaten on most image related tasks by deep learning algorithms, we study a classification method that can solve jigsaw puzzle…
LoCo learns local representations without end-to-end synchronization, improving performance on complex tasks.
Evolutionary algorithms improve decision tree ensembles.
Greedy algorithm achieves sublinear regret for various distributions.
Greedy algorithm optimizes consumption habits with power utility.
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal,…
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of -sparse signals b…
New distributions allow greedy arm selection in sparse bandit problems.
A new method quantifies feature-map discriminativeness for efficient pruning of deep neural networks.
Simpler ε-greedy with longer action durations improves exploration.
This paper is a follow up to the previous author's paper on convex optimization. In that paper we began the process of adjusting greedy-type algorithms from nonlinear approximation for finding sparse solutions of convex optimization problems. We modified there three the most popular in nonlinear approximation in Banach…
Introduces greedy feature selection for classifier-dependent feature ranking.
Greedy selection works well in a toy model of independent increments.
Greedy policy maximizes information in unknown linear systems.
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness is set at maximum, KES co…
Meta-AAD uses deep reinforcement learning to improve anomaly detection by selecting the most informative instances.
Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work \cite{efroni2018beyond}, multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analy…
Paper analyzes Greedy-GQ for reinforcement learning with Markovian noise.
New algorithm speeds up learning of graphical models.
Improved greedy 2-coordinate updates for optimization problems with constraints.
We propose a new concept named adaptive submodularity ratio to study the greedy policy for sequential decision making. While the greedy policy is known to perform well for a wide variety of adaptive stochastic optimization problems in practice, its theoretical properties have been analyzed only for a limited class of p…
The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical setti…
We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The…
Greedy algorithm nearly outperforms exploration in contextual bandits.
New greedy algorithms improve Bayesian optimisation performance.
A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient se…
Greedy training of recursive partitioning estimators faces a computational barrier when the true function doesn't satisfy a specific property.
In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note that this is also equivalent to recovering the underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We present two no…
Greedy method adds neurons one by one for better function approximation.
Breathing k-means outperforms greedy k-means++ in clustering.
The paper gives a systematic study of the approximate versions of three greedy-type algorithms that are widely used in convex optimization. By approximate version we mean the one where some of evaluations are made with an error. Importance of such versions of greedy-type algorithms in convex optimization and in approxi…
Greedy algorithm performs well in online matching despite non-i.i.d. connections.
We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in …
Study on rapid policy changes in reinforcement learning.
This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in that…
Greedy algorithms outperform UCB in many-armed bandit problems.
Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of even greedy algorithms can be quite high. This is because for each greedy step we need to refit a model or calculate a function using the pre…
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obtain a shallow network by utilizing a greedy algorithm with the prescribed dictionary provided by the available training data and a set of po…
Greedy policies perform poorly in imperfectly observed contextual bandits.