English
Related papers

Related papers: Learning Multinomial Logits in $O(n \log n)$ time

200 papers

Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular $k$NN classifier. However, its resulting metric relies on pre-selected target neighbors. In this paper, we address the feasibility of…

Data Structures and Algorithms · Computer Science 2018-05-03 Babak Hosseini , Barbara Hammer

We study a general model on reusable resource allocation under model uncertainty. A heterogeneous population of customers arrive at the decision maker's (DM's) platform sequentially. Upon observing a customer's type, the DM selects an…

Optimization and Control · Mathematics 2022-12-07 Xilin Zhang , Wang Chi Cheung

Multi-task learning (MTL) is a subfield of machine learning with important applications, but the multi-objective nature of optimization in MTL leads to difficulties in balancing training between tasks. The best MTL optimization methods…

Machine Learning · Computer Science 2021-09-20 Michael Crawshaw , Jana Košecká

The rapid advancement in large language models (LLMs) has brought forth a diverse range of models with varying capabilities that excel in different tasks and domains. However, selecting the optimal LLM for user queries often involves a…

Machine Learning · Computer Science 2025-02-06 Yang Li

In applications such as recommendation systems and revenue management, it is important to predict preferences on items that have not been seen by a user or predict outcomes of comparisons among those that have never been compared. A popular…

Machine Learning · Computer Science 2015-06-29 Sewoong Oh , Kiran K. Thekumparampil , Jiaming Xu

The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called…

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over…

Machine Learning · Computer Science 2020-10-23 Giuseppe Marra , Ondřej Kuželka

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio…

Atmospheric and Oceanic Physics · Physics 2009-01-29 Jochen Bröcker

Speculative sampling reduces the latency of autoregressive decoding for target model LLMs without sacrificing inference quality, by using a cheap draft model to suggest a candidate token and a verification criterion to accept or resample…

Machine Learning · Computer Science 2025-11-21 Rahul Krishna Thomas , Arka Pal

Transformer attention scales quadratically with sequence length O(n^2), limiting long-context use. We propose Adaptive Retention, a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict…

Computation and Language · Computer Science 2025-10-13 S M Rafiuddin , Muntaha Nujat Khan

Model Agnostic Meta Learning or MAML has become the standard for few-shot learning as a meta-learning problem. MAML is simple and can be applied to any model, as its name suggests. However, it often suffers from instability and…

Machine Learning · Computer Science 2024-11-04 JuneYoung Park , MinJae Kang

Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of $n$ items from a data universe equipped with a total order, the task is to compute a sketch (data…

Data Structures and Algorithms · Computer Science 2023-08-25 Graham Cormode , Zohar Karnin , Edo Liberty , Justin Thaler , Pavel Veselý

Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after adjustments for factors like length. Unfortunately, LLMs may…

Computation and Language · Computer Science 2024-06-12 Aidar Myrzakhan , Sondos Mahmoud Bsharat , Zhiqiang Shen

Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict. Datasets often exhibit multimodality, intransitivity, or incomplete rankings---particularly when generated by humans---yet…

Machine Learning · Computer Science 2019-01-29 Stephen Ragain , Johan Ugander

\textit{Mallows model} is a widely-used probabilistic framework for learning from ranking data, with applications ranging from recommendation systems and voting to aligning language models with human preferences~\cite{chen2024mallows,…

Machine Learning · Statistics 2025-07-14 Yeganeh Alimohammadi , Kiana Asgari

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's…

Information Retrieval · Computer Science 2019-08-22 Fan Liu , Zhiyong Cheng , Changchang Sun , Yinglong Wang , Liqiang Nie , Mohan Kankanhalli

In digital health and EdTech, recommendation systems face a significant challenge: users often choose impulsively, in ways that conflict with the platform's long-term payoffs. This misalignment makes it difficult to effectively learn to…

Machine Learning · Computer Science 2024-02-22 Arpit Agarwal , Rad Niazadeh , Prathamesh Patil

State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and parameters. In this work, we focus on parameter inference,…

Computation · Statistics 2026-05-22 Kostas Tsampourakis , Víctor Elvira

Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially in the number of agents. These difficulties are exacerbated…

Machine Learning · Computer Science 2025-10-27 Emile Anand , Ishani Karmarkar , Guannan Qu

Estimating consumer preferences is central to many problems in economics and marketing. This paper develops a flexible framework for learning individual preferences from partial ranking information by interpreting observed rankings as…

Machine Learning · Statistics 2026-02-19 Yu-Chang Chen , Chen Chian Fuh , Shang En Tsai