A robot learns object categories using multimodal data and selects actions efficiently.
problem Efficiently recognize object categories using multimodal data in real-time.
method Multimodal Hierarchical Dirichlet Process (MHDP) with information gain maximization and lazy greedy algorithm.
result The method selects actions that allow quick and accurate recognition of target objects.
Framework uses low-dimensional maps to solve high-dimensional Bayesian inference problems.
problem High-dimensional Bayesian inference problems.
method Structure-exploiting lazy maps and flows, focusing on low-dimensional subspace.
result Weak convergence of generated distributions to the posterior.
Apricot selects subsets from large data sets efficiently using submodular optimization.
problem Efficiently selecting representative subsets from large data sets.
method Submodular optimization with efficient greedy algorithm.
result Strong theoretical guarantees on the quality of selected subsets.
Lazy-CFR improves CFR's efficiency and performance in imperfect information games.
problem Efficiency and performance in imperfect information games with imperfect information.
method Lazy update technique to avoid full traversal of game tree, resulting in a more efficient CFR variant.
result Lazy-CFR achieves better convergence and significantly outperforms vanilla CFR in experiments.
Unified formula for training dynamics of linear networks combining lazy and balanced regimes.
problem Training dynamics of linear networks in two distinct setups: lazy and balanced/active.
method Unified formula for the evolution of the learned matrix, combining lazy and balanced regimes.
result Unified formula allows for rapid convergence and low rank bias, proving a complete phase diagram.
Lazy SPCA simplifies SPCA for large datasets with similar performance.
problem Efficiently reducing high-dimensional datasets for large-scale computations.
method Derives a simplified algorithm (Lazy SPCA) with reduced computational complexity.
result Lazy SPCA finds the same principal subspace as SPCA and maintains similar pairwise distances.
Lazy neural networks are vulnerable to adversarial attacks.
problem Adversarial robustness issues in lazy training models.
method Extending recent work on adversarial examples to lazy training models.
result Over-parametrized neural networks that generalize well remain vulnerable to single-step gradient ascent attacks.
Study introduces a new investment strategy model using lazy factor and probability weights.
problem Optimizing investment strategies in volatile markets with transaction costs.
method Combines Price Portfolio Forecasting and Mean-Variance Models with Transaction Costs, using probability weights as laziness factor coefficients.
result Model demonstrates adaptability and generalizability in transforming investment strategies.
A new method simulates a lazy version of a Markov chain for empirical inference.
problem Estimating and testing unknown Markov chains with limited data.
method Simulates an α-lazy version of an unknown Markov chain, making it ergodic.
result The pseudo spectral gap can be applied to non-ergodic Markov chains.
Gradient descent can find better tensor decompositions than lazy training in over-parameterized settings.
problem Finding better tensor decompositions in over-parameterized settings.
method Gradient descent on over-parameterized tensor decomposition problems.
result Gradient descent can find an approximate tensor decomposition with rank m=O∗(r2.5llogd), while lazy training requires m=Ω(dl−1). Improved SGD algorithm with faster convergence.
problem Optimization of machine learning models.
method Conditional accelerated lazy stochastic gradient descent.
result Convergence rate of $O\left(\frac{1}{\varepsilon^2}
ight)$, faster than previous methods.
Lazy, perfectly informed investors trade infrequently due to costs.
problem The paradox of an omniscient yet lazy investor trading infrequently.
method Formalized the paradox using geometric and fractional Brownian motion models, derived closed-form profit functions, and proved existence and uniqueness of the optimal trading frequency.
result The optimal trading frequency can be interpreted through the fractal dimension of the price path.
Lazy training and mean field regimes studied for TD learning with nonlinear function approximation.
problem Approximating value function for MRP with TD learning and nonlinear functions.
method Lazy training and mean field scaling of parameters analyzed for convergence.
result Lazy training leads to exponential convergence to local/global minimizers, while mean field scaling results in all fixed points being minimizers.
New algorithm reduces regret and constraint violation in online convex optimization with predictions.
problem Online convex optimization with time-varying constraints and predictions.
method Primal-dual algorithm combining Follow-The-Regularized-Leader with adaptive steps.
result Achieves O(T43−β) regret and O(T21+β) constraint violation bounds. New private algorithms for online learning improve regret in high privacy regimes.
problem Private online learning from experts and convex optimization.
method Transformed lazy algorithms for differential privacy.
result Improved regret bounds for DP-OPE and DP-OCO.
Quantum machine learning faces 'laziness' and 'barren plateaus', but noise can mitigate the latter.
problem Quantum machine learning's loss function landscape issues.
method Theoretical analysis of quantum variational circuits, neural tangent kernels, and noise effects.
result Noise can mitigate barren plateaus in quantum machine learning.
Lazy Gradient Descent outperforms existing polytope algorithms in pseudo-regret.
problem Achieving optimal regret bounds on polytopes efficiently.
method Lazy Online Gradient Descent on polytopes.
result Proves O(1) pseudo-regret against i.i.d opponents. Deep networks prioritize easier examples over harder ones, leading to faster training.
problem Understanding how deep networks prioritize examples of varying difficulty.
method Investigated the effect of linear vs non-linear learning modes on example difficulty.
result Non-linear dynamics tend to sequentialize the learning of examples of increasing difficulty.
Study on optimizing model updates in performative prediction.
problem Optimizing model updates influenced by model predictions.
method Stochastic optimization with greedy and lazy deploy approaches.
result Rates of convergence for both greedy and lazy deploy methods.
Grokking occurs when neural networks transition from lazy to rich training dynamics, fitting initial features before generalizing.
problem Understanding why neural networks exhibit early train loss decrease without corresponding test loss improvement.
method Analyzing vanilla gradient descent on polynomial regression with a two-layer neural network, identifying sufficient statistics for test loss.
result Grokking arises when a network first attempts to fit a kernel regression solution with initial features, followed by late-time feature learning.
Study shows optimal model performance at critical level of feature learning.
problem Catastrophic forgetting in neural networks, especially in non-stationary environments.
method Systematic study on model scale and feature learning, using dynamical mean field theory.
result Optimal performance achieved at a critical level of feature learning, dependent on task non-stationarity and model scale.
Lazy labels enable deep learning for microscopy segmentation without full annotation.
problem Lack of pixel-wise annotations limits fully supervised learning for bioimage segmentation.
method Lazy labels combined with coarse labels for training a deep neural network.
result Model achieves accurate segmentation with minimal pixel-wise annotations.
Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.
problem Understanding the behavior of network-valued stochastic processes with asynchronous updates.
method Analysis of concentration properties of aggregated adjacency and Laplacian matrices for lazy network-valued stochastic processes.
result Demonstrates consistency of estimators in community and changepoint estimation problems.
The study distinguishes between lazy and feature training limits in deep neural networks.
problem Understanding the dynamics of deep neural networks as they grow in width.
method Varying the scaling of weights in the last layer and observing the crossover between two limits.
result Two distinct limits (NTK and Mean-Field) are identified, each with its own dynamics and kernel evolution.
Proposes an efficient algorithm for identifying important features in binary classification.
problem Understanding explainability of deep neural networks in binary classification.
method Variable-importance framework combined with lazy training.
result Achieves well-controlled error rates with minimal assumptions.
New method converts and optimizes sampling schedules for generative models.
problem Optimizing sampling schedules for generative models like flows and diffusions.
method Unified framework for stochastic interpolants, including point mass schedules.
result Demonstrated efficient generation of images with fewer steps.
Clapping reduces memory usage in distributed optimization by reusing data samples.
problem Significant communication overhead and impractical memory overhead in pipeline-parallel distributed optimization.
method Lazy sampling strategy to reuse data samples across steps, supporting convergence without unbiased gradient assumptions.
result Clapping achieves convergence in few-epoch or online training regimes without sample-size memory overhead.
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more con…
ABC algorithms involve a large number of simulations from the model of interest, which can be very computationally costly. This paper summarises the lazy ABC algorithm of Prangle (2015), which reduces the computational demand by abandoning many unpromising simulations before completion. By using a random stopping decis…
LazyBum uses lazy propositionalization to learn from relational data.
problem Applying propositional learners to relational data.
method Interleaves decision tree learning with lazy propositionalization.
result Achieves comparable accuracy to other methods with lower execution time.
New algorithm for active exploration in bandit problems.
problem Active exploration in bandit problems.
method Gradient ascent with online lazy mirror ascent sampling rule.
result Asymptotically optimal and computationally efficient.
We explore how neural networks train to zero loss, focusing on initial scale.
problem Understanding neural network training dynamics and zero loss.
method Macroscopic limits analysis of gradient descent dynamics.
result Gradient descent can drive deep neural networks to zero loss regardless of initialization.
A method learns common bias for multiple low-variance tasks without hyper-parameter tuning.
problem Learning common bias for multiple low-variance tasks without manual tuning.
method Two variants of online learning methods (aggressive and lazy) that update bias after each datapoint or at the end of each task.
result Across-tasks regret bound derived for the method, showing faster rates for aggressive variant and standard rates for lazy variant.
Study on adversarial robustness in neural networks across initialization and training phases.
problem Understanding adversarial robustness in neural networks during different learning stages.
method Analyzes adversarial robustness in various scenarios of over-parameterized networks with quadratic targets and infinite samples.
result Robustness can worsen when test error improves, and vice versa, revealing new tradeoffs.
RAmmStein optimizes liquidity management in AMMs by learning to rebalance efficiently.
problem Optimal control of concentrated liquidity in decentralized exchanges.
method Formulates as an optimal control problem, uses Deep Reinforcement Learning with HJB-QVI.
result Achieves highest net ROI (1.60%) compared to greedy strategies, reduces rebalancing frequency by 85%.
Transformers learn rich in-context dependencies efficiently.
problem Understanding how transformers learn long-range dependencies efficiently.
method Approximation and dynamics analysis of induction head mechanisms.
result Abrupt transition from lazy to rich mechanisms during training.
Deep and wide ReLU networks learn data-dependent features even in the lazy training regime.
problem Understanding the behavior of neural networks with finite depth and width.
method Analyzing the mean and variance of the neural tangent kernel (NTK) in a randomly initialized ReLU network.
result The NTK has a non-trivial evolution during training, with the mean of its first SGD update being exponential in the ratio of depth to width.
We study Bayesian optimal control of a general class of smoothly parameterized Markov decision problems. Since computing the optimal control is computationally expensive, we design an algorithm that trades off performance for computational efficiency. The algorithm is a lazy posterior sampling method that maintains a d…
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
Analyzes neural networks using linear models to understand their behavior.
problem Understanding multi-layer neural networks through linear models.
method Recalls and reviews four models: linear regression with concentrated features, kernel ridge regression, random feature model, and neural tangent model.
result Highlights limitations of linear theory and discusses approaches to overcome them.
LAZO reduces query complexity and variance in ZO methods.
problem High query complexity and variance in zeroth-order optimization.
method LAZO uses adaptive lazy queries to reduce variance and save queries.
result LAZO achieves lower regret and query complexity compared to existing methods.
Deep learning and lazy learner improve early sepsis detection.
problem Detecting sepsis early in high-resolution ICU records.
method Deep learning model with temporal convolutional network and Gaussian Process Adapter; lazy learner with dynamic time warping.
result Improves sepsis detection from 0.25 to 0.40 AUPRC 7 hours before onset.
LazyDINO efficiently solves high-dimensional Bayesian inverse problems with fast and scalable solutions.
problem High-dimensional nonlinear Bayesian inverse problems with expensive parameter-to-observable maps.
method LazyDINO combines derivative-informed neural surrogates and lazy map variational inference for efficient posterior approximation.
result Significant cost reduction in amortized Bayesian inversion, achieving one to two orders of magnitude improvement.
Study on rich regime training in deep learning, finding active parameters in bottom layers.
problem Understanding the practical success of deep learning models.
method Empirical study on rich regime training with benchmark datasets, re-initialization analysis, and probabilistic Layer-Wise Sparse SGD.
result Probabilistic Layer-Wise Sparse SGD matches vanilla SGD's generalization performance with improved efficiency.
Auto-CEI improves LLM reasoning by balancing assertiveness and conservativeness.
problem Hallucinations and laziness in LLM reasoning tasks.
method Expert Iteration explores reasoning trajectories, guiding incorrect paths back on track and promoting appropriate 'I don't know' responses.
result Auto-CEI achieves superior alignment in logical reasoning, mathematics, and planning tasks.
Mirror flow in shallow neural networks shows similar implicit bias to gradient flow, with key differences in curvature penalties.
problem Analyzing implicit bias in shallow neural networks with mirror flow.
method Characterization through variational problems and scaled potentials.
result Mirror flow with scaled potentials induces a rich class of biases not captured by RKHS norms.
Regularized greedy policies outperform classical greedy in finite-horizon bandit problems.
problem Optimizing decision-making in sequential experiments with finite time constraints.
method Developed regularized greedy algorithms for multi-armed Bernoulli bandits.
result Calibrated regularized greedy policies consistently match or outperform state-of-the-art algorithms.
Use simplified layerwise linear models to understand neural dynamics.
problem Complex neural network dynamics are hard to grasp.
method Apply simplified layerwise linear models to explain neural phenomena.
result Simplified models explain neural collapse, emergence, etc.