A new method assigns hidden parameters deterministically to improve learning efficiency.
problem Traditional learning methods struggle with high computational burden.
method Two-stage learning with deterministic assignment of hidden parameters.
result Deterministic assignment of hidden parameters almost matches traditional learning's generalization performance.
The paper presents a method to assign accurate and calibrated uncertainties to deterministic model predictions.
problem Assigning uncertainties to deterministic model predictions.
method Transforming deterministic predictions into probabilistic ones using a cost function that balances accuracy and reliability.
result The method improves the reliability of probabilistic predictions without sacrificing accuracy.
Study on how deterministic dependencies affect information synergy and redundancy.
problem Understanding how deterministic dependencies impact information synergy and redundancy.
method Systematic analysis of deterministic dependencies on information decomposition.
result Identifies how negative terms can originate from deterministic dependencies and discusses implications for neural coding.
A model explains credit assignment in deep learning networks.
problem Understanding how deep learning assigns credit to parameters.
method Mean-field learning model with ensembles of sub-networks.
result Synaptic connections can be categorized into three types.
Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of conditionally conjugate exponential family models. This approach uses a decreasing te…
Paper introduces a simple method to assign uncertainty in contrastive learning models.
problem Contrastive learning models lack uncertainty measures.
method Trains a deep network to assign uncertainty based on representation variance.
result Deep uncertainty model improves anomaly detection and out-of-distribution classification.
New algorithm clusters sparse data effectively.
problem Challenges in clustering sparse data.
method Deterministic Information Bottleneck framework for joint feature weighting and clustering.
result Demonstrated effectiveness on real-world genomics data.
New STDP rule for spiking neurons solves discrete action reinforcement learning tasks.
problem Applying standard STDP to discrete action reinforcement learning tasks.
method Feedback-modulated TD-STDP learning rule for spiking neuron networks.
result Feedback modulation improves credit assignment in reinforcement learning.
Bayesian networks (BNs) are graphical models that are useful for representing high-dimensional probability distributions. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a BN from observed data. Typically, one assigns a score to various structures and the sear…
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outl…
This note fills the gap in market-consistent valuation of lifelong health insurance products.
problem Market-consistent valuation of lifelong health insurance products is not well-addressed.
method Constructs a valuation portfolio to separate Best Estimate into policy data and financial instrument prices.
result The Best Estimate valuation is not uniquely determined by prevailing term structures and requires a stochastic model.
Bayesian Neural Nets improve model stability and fit.
problem Improving model stability and fit in time series prediction.
method Assign Bayesian Neural Nets to drift and diffusion terms of SDE, infer posterior using SGLD.
result Significantly improved stability and better model fit on benchmarks.
Paper evaluates how forecast errors affect optimal utilisation in production planning.
problem Forecast errors impact optimal utilisation in production planning.
method Simulation and mixed integer programming for stochastic demand.
result Forecast errors significantly affect optimal costs in production planning.
Probabilistic fair clustering tackles uncertain group membership.
problem Fair clustering with imperfect group membership.
method Probabilistic algorithms for metric graphs and metric membership.
result Approximation ratio guarantees for fair clustering.
Unified view on selective credit assignment for reinforcement learning.
problem Efficient credit assignment in reinforcement learning.
method Unified temporal-difference algorithms with selective weightings.
result New algorithms for backward credit assignment and off-policy learning.
Same homology via different sign assignments in link Floer theory.
problem Comparing sign assignments in link Floer homology.
method Comparison of sign assignments from two different constructions.
result Small modification of sign convention results in identical chain complexes.
Text clustering method replaces centroids with summaries for interpretability and scalability.
problem Efficiently clustering text data while maintaining interpretability and scalability.
method k-NLPmeans and k-LLMmeans, which periodically replace numeric centroids with textual summaries.
result Consistently outperforms classical baselines and recent LLM-based clustering methods.
New algorithms assign credit to past decisions based on hindsight.
problem Efficient credit assignment in reinforcement learning.
method Explicit credit assignment using new data in hindsight.
result Value functions can be rewritten to include hindsight credit assignment.
Developed mlf-core for deterministic machine learning.
problem Ensuring machine learning models are deterministic for verification.
method Formulated requirements, developed mlf-core ecosystem, tested various models.
result Demonstrated deterministic models in biomedical fields.
EgalMAB solves fair resource allocation in stochastic bandits.
problem Fair resource allocation in a stochastic multi-armed bandit setting.
method Design and analysis of UCB-based policy EgalUCB.
result Established upper bounds on cumulative regret.
Assignment methods are at the heart of many algorithms for unsupervised learning and clustering - in particular, the well-known K-means and Expectation-Maximization (EM) algorithms. In this work, we study several different methods of assignment, including the "hard" assignments used by K-means and the ?soft' assignment…
Error bounds based on worst likely assignments use permutation tests to validate classifiers. Worst likely assignments can produce effective bounds even for data sets with 100 or fewer training examples. This paper introduces a statistic for use in the permutation tests of worst likely assignments that improves error b…
COCOA improves credit assignment in reinforcement learning by measuring contributions to rewards.
problem Improving sample efficiency in reinforcement learning through better credit assignment methods.
method Counterfactual Contribution Analysis (COCOA) for precise credit assignment.
result COCOA achieves lower bias and variance compared to Hindsight Credit Assignment (HCA), improving reinforcement learning performance.
Study on regret minimization in deterministic MDPs.
problem Minimizing regret in deterministic reinforcement learning.
method Logarithmic regret lower bounds, leveraging graph theory and cycles.
result Explicitly quantifies the fundamental limit of performance achievable by any learning algorithm.
Paper improves reinforcement learning efficiency with deterministic value gradients.
problem High sample complexity in model-free DDPG algorithms for continuous control tasks.
method Proposes DVG and DVPG algorithms with infinite horizon value gradients to improve sample efficiency.
result DVPG algorithm substantially outperforms state-of-the-art methods on continuous control benchmarks.
New methods optimize personalized treatment assignment in trials with many arms.
problem Poor performance of standard methods in trials with many treatment arms.
method Regularized and clustered joint assignment forest algorithm.
result Gains in predicting arm-wise outcomes and utility gains from personalization.
CGAN fails to improve deterministic sequence predictions, revealing a theoretical limitation.
problem Improving deterministic sequence predictions with CGAN.
method Developed an adversarial content loss approach.
result CGAN does not improve deterministic sequence predictions.
Proposes unbiased estimators for training mixture of experts models.
problem Efficiently training large-scale mixture of experts models on modern hardware.
method Two unbiased estimators based on principled stochastic assignment procedures.
result Both estimators are more effective and robust than biased alternatives.
Optimizes balanced treatment assignment for experiments.
problem Balancing treatment groups in experiments for optimal results.
method Optimization of a two-sample test, using minimum spanning tree test.
result Optimal assignment algorithm with polynomial time complexity.
Counterfactual learning improves SMT by smoothing out deterministic logs.
problem Deterministic logging limits exploration in SMT systems.
method Additive and multiplicative control variates to smooth out deterministic components.
result Improvements of up to 2 BLEU points achieved through counterfactual learning.
The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are ob…
ProMP improves Meta-RL by better handling credit assignment.
problem Poor credit assignment in Meta-RL leads to suboptimal performance.
method Develops a novel meta-learning algorithm controlling pre-adaptation and adapted policies.
result Significantly improves sample-efficiency and asymptotic performance.
Paper combines deterministic and stochastic inference methods for PGMs.
problem Combining biases from deterministic methods and high costs from Monte Carlo.
method Sequential Monte Carlo algorithm that uses output from deterministic approximations.
result Improves upon deterministic methods and Monte Carlo by reducing biases and computational costs.
A new Q&A labeling method for assigning labels in machine learning.
problem Assigning labels to instances in supervised machine learning.
method Developed a Q&A labeling method involving a question generator and an annotator.
result The derived label generative model is consistent with previous studies.
Study compares methods for treatment assignment, finding A-learner best for playlist generation.
problem Treatment assignment in various applications.
method Three classes of algorithms: O-learner, E-learner, A-learner.
result Optimizing for outcomes or causal effects does not lead to optimal treatment assignments.
This letter tackles channel assignment in uplink wireless communication systems.
problem Maximizing the sum rate of all users in uplink wireless communication systems with integer channel assignment constraints.
method A convex optimization based algorithm is used to find the optimal channel assignment. Machine learning approaches, including CNNs, FNNs, random forest, and GRUs, are employed to reduce computation time.
result Machine learning methods largely reduce computation time with slightly compromised prediction accuracy.
Solves partial assignment problems using random clique complexes.
problem Partial assignment problems, especially with severe occlusions and distortions.
method Formulate as matching random clique complexes, analyze k-skeletons, match adjacency matrices, consider geometric neighbourhoods.
result Outperforms diverse matching algorithms significantly.
We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity mat…
Linear-time algorithm for optimal assignment in graph matching.
problem Finding optimal assignments between graph vertices efficiently.
method Developed an algorithm for linear-time optimal assignment using tree distances.
result Approximated edit distance between graphs in linear time.
We convert deterministic flow models to stochastic samplers.
problem Deterministic flow models are sensitive to errors and cannot condition on intermediate states.
method Transform ODEs into SDEs with the same marginal distributions.
result Empirically outperforms deterministic samplers and controls generation diversity.
Study on optimal rates for sequential probability assignment using smoothed analysis.
problem Optimal rates for sequential probability assignment under smoothed adversaries.
method General-purpose reduction from minimax rates to transductive learning, development of an efficient algorithm using MLE oracle.
result Optimal (logarithmic) fast rates for parametric and finite VC dimension classes, sublinear regret for general classes.
Study shows deterministic equivalent for neural network kernel convergence.
problem Understanding convergence of neural network kernels.
method Analyzes empirical spectral distribution of Conjugate Kernel, proving convergence to a deterministic limit.
result Obtains a deterministic equivalent for the Stieltjes transform and resolvent of the Conjugate Kernel.
New methods estimate policy value and gradients for deterministic policies from off-policy data.
problem Estimating policy value and gradients for deterministic policies from off-policy data.
method Proposed new doubly robust estimators based on kernelization approaches.
result Demonstrated a rate independent of horizon length for policy value and gradient estimation.
New method for reinforcement learning with deterministic state transitions.
problem Generalizing reinforcement learning to include deterministic state transitions.
method Introducing GDPG algorithm combining model-based and model-free techniques.
result GDPG algorithm significantly outperforms existing methods in continuous control benchmarks.
Eigenoptions improve credit assignment in reinforcement learning.
problem Improving credit assignment in reinforcement learning models.
method Investigated eigenoptions for credit assignment in model-free RL, comparing pre-specified and online discovery methods.
result Pre-specified eigenoptions aid exploration and credit assignment, while online discovery can hinder learning.
Study on knots using 17 colors, finding specific color assignments.
problem Understanding the minimum number of colors needed for Fox colorings of knots.
method Investigated 17-colorable knots and their diagrams.
result Found that exactly 6 out of 17 colors are used in diagrams of 17-colorable knots.
Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.
problem Accelerating convex optimization
method Hamiltonian dynamics
result Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.
Develops DPG methods for continuous-time RL with deterministic policies.
problem High variance and slow convergence in stochastic policy RL methods.
method Derives continuous-time policy gradient formula and proposes CT-DDPG algorithm.
result CT-DDPG achieves superior stability and faster convergence in continuous-time RL.