Search Results for author: Sebastian Tschiatschek

Found 41 papers, 11 papers with code

Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions

no code implementations24 Jan 2024 Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek

Explanations of AI systems rarely address the information needs of people affected by algorithmic decision-making (ADM).

Decision Making

Active Third-Person Imitation Learning

no code implementations27 Dec 2023 Timo Klein, Susanna Weinberger, Adish Singla, Sebastian Tschiatschek

We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert.

Active Learning Generative Adversarial Network +2

Posterior Consistency for Missing Data in Variational Autoencoders

no code implementations25 Oct 2023 Timur Sudak, Sebastian Tschiatschek

We consider the problem of learning Variational Autoencoders (VAEs), i. e., a type of deep generative model, from data with missing values.

Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming

no code implementations15 Oct 2023 Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla

We instantiate several methods based on LLM-SS framework and evaluate them using an existing benchmark, StudentSyn, for student attempt synthesis in a visual programming domain.


Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

no code implementations26 May 2023 Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences.

Decision Making

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

1 code implementation28 Mar 2023 Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla

We introduce a scaffolding framework based on pop quizzes presented as multi-choice programming tasks.

Interactively Learning Preference Constraints in Linear Bandits

1 code implementation10 Jun 2022 David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case.

Decision Making

Option Transfer and SMDP Abstraction with Successor Features

no code implementations18 Oct 2021 Dongge Han, Sebastian Tschiatschek

Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning.

Reinforcement Learning (RL)

Contextual HyperNetworks for Novel Feature Adaptation

no code implementations12 Apr 2021 Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang

While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension.

Few-Shot Learning Imputation +1

Replication-Robust Payoff-Allocation for Machine Learning Data Markets

no code implementations25 Jun 2020 Dongge Han, Michael Wooldridge, Alex Rogers, Olga Ohrimenko, Sebastian Tschiatschek

In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication.

BIG-bench Machine Learning

AMRL: Aggregated Memory For Reinforcement Learning

no code implementations ICLR 2020 Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann

In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy.

reinforcement-learning Reinforcement Learning (RL)

Educational Question Mining At Scale: Prediction, Analysis and Personalization

no code implementations12 Mar 2020 Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, Jose Miguel Hernandez-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students.

Resource-Efficient Neural Networks for Embedded Systems

no code implementations7 Jan 2020 Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches.

Autonomous Navigation BIG-bench Machine Learning +2

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

1 code implementation NeurIPS 2019 Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.

BIG-bench Machine Learning Imputation +1

Collaborative Machine Learning Markets with Data-Replication-Robust Payments

no code implementations8 Nov 2019 Olga Ohrimenko, Shruti Tople, Sebastian Tschiatschek

We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data.

BIG-bench Machine Learning

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model

1 code implementation13 Aug 2019 Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang

In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.

Active Learning BIG-bench Machine Learning +2

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

no code implementations NeurIPS 2019 Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla

We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.

reinforcement-learning Reinforcement Learning (RL)

Teaching Inverse Reinforcement Learners via Features and Demonstrations

no code implementations NeurIPS 2018 Luis Haug, Sebastian Tschiatschek, Adish Singla

In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i. e., where there is a mismatch between the worldviews of the learner and the expert.

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

1 code implementation ICLR 2019 Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.

Decision Making Experimental Design +1

Sum-Product Networks for Sequence Labeling

no code implementations6 Jul 2018 Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf

We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and output-dependent factors.

Optical Character Recognition Optical Character Recognition (OCR)

Variational Inference for Data-Efficient Model Learning in POMDPs

no code implementations23 May 2018 Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann

In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference.

Decision Making Decision Making Under Uncertainty +2

Fake News Detection in Social Networks via Crowd Signals

no code implementations24 Nov 2017 Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, Andreas Krause

The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.

Social and Information Networks

Learning User Preferences to Incentivize Exploration in the Sharing Economy

no code implementations17 Nov 2017 Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause

We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb.

Coordinated Online Learning With Applications to Learning User Preferences

no code implementations9 Feb 2017 Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause

We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners.

Multi-Task Learning

Cooperative Graphical Models

no code implementations NeurIPS 2016 Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause

We study a rich family of distributions that capture variable interactions significantly more expressive than those representable with low-treewidth or pairwise graphical models, or log-supermodular models.

Variational Inference

Variational Inference in Mixed Probabilistic Submodular Models

no code implementations NeurIPS 2016 Josip Djolonga, Sebastian Tschiatschek, Andreas Krause

We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials.

Variational Inference

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

no code implementations23 May 2016 Adish Singla, Sebastian Tschiatschek, Andreas Krause

We propose an active learning algorithm that substantially reduces this sample complexity by exploiting the structural constraints on the version space of hemimetrics.

Active Learning

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization

no code implementations23 Nov 2015 Adish Singla, Sebastian Tschiatschek, Andreas Krause

When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize.

Learning Mixtures of Submodular Functions for Image Collection Summarization

no code implementations NeurIPS 2014 Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes

This paper provides, to our knowledge, the first systematic approach for quantifying the problem of image collection summarization, along with a new dataset of image collections and human summaries.

Document Summarization Structured Prediction

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