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Found 23 papers, 4 papers with code
Explainable k-Means and k-Medians Clustering
In terms of negative results, we show that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and we prove that any explainable clustering must incur an \Omega(\log k) approximation compared to the optimal clustering.
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns.
Gradient-Free Neural Network Training on the Edge
Training neural networks is computationally heavy and energy-intensive.
An Axiomatic Approach to Model-Agnostic Concept Explanations
Concept explanation is a popular approach for examining how human-interpretable concepts impact the predictions of a model.
Principal-Agent Reward Shaping in MDPs
Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest.
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes
Interpretability is an essential building block for trustworthiness in reinforcement learning systems.
XAudit : A Theoretical Look at Auditing with Explanations
This work formalizes the role of explanations in auditing and investigates if and how model explanations can help audits.
Finding Safe Zones of policies Markov Decision Processes
Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset.
Framework for Evaluating Faithfulness of Local Explanations
We study the faithfulness of an explanation system to the underlying prediction model.
Online $k$-means Clustering on Arbitrary Data Streams
We propose a data parameter, $\Lambda(X)$, such that for any algorithm maintaining $O(k\text{poly}(\log n))$ centers at time $n$, there exists a data stream $X$ for which a loss of $\Omega(\Lambda(X))$ is inevitable.
Connecting Interpretability and Robustness in Decision Trees through Separation
We then show that a tighter bound on the size is possible when the data is linearly separated.
Bounded Memory Active Learning through Enriched Queries
The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select informative examples in order to lower prohibitively expensive labeling costs.
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
We give the first algorithm for this setting that obtains a constant approximation factor on the optimal risk under a random arrival order, an exponential improvement over previous work.
No-substitution k-means Clustering with Adversarial Order
We also prove that if the data is sampled from a ``natural" distribution, such as a mixture of $k$ Gaussians, then the new complexity measure is equal to $O(k^2\log(n))$.
Towards a Combinatorial Characterization of Bounded-Memory Learning
We propose a candidate solution for the case of realizable strong learning under a known distribution, based on the SQ dimension of neighboring distributions.
ExKMC: Expanding Explainable $k$-Means Clustering
To allow flexibility, we develop a new explainable $k$-means clustering algorithm, ExKMC, that takes an additional parameter $k' \geq k$ and outputs a decision tree with $k'$ leaves.
Explainable $k$-Means and $k$-Medians Clustering
In terms of negative results, we show, first, that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and second, that any tree-induced clustering must in general incur an $\Omega(\log k)$ approximation factor compared to the optimal clustering.
Towards a combinatorial characterization of bounded memory learning
In this paper we aim to develop combinatorial dimensions that characterize bounded memory learning.
Unexpected Effects of Online no-Substitution k-means Clustering
For example, for k-means cost with constant k>1 and random order, Theta(log n) centers are enough to achieve a constant approximation, while the mere a priori knowledge of n reduces the number of centers to a constant.
Novel Uncertainty Framework for Deep Learning Ensembles
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression.
A General Memory-Bounded Learning Algorithm
Designing bounded-memory algorithms is becoming increasingly important nowadays.
Mixing Complexity and its Applications to Neural Networks
We suggest analyzing neural networks through the prism of space constraints.
Principled Option Learning in Markov Decision Processes
It is well known that options can make planning more efficient, among their many benefits.
