@conference {Jancsary2013,
	title = {Learning convex QP relaxations for structured prediction},
	booktitle = {30th International Conference on Machine Learning, ICML 2013},
	number = {PART 3},
	year = {2013},
	pages = {1952{\textendash}1960},
	abstract = {We introduce a new large margin approach to discriminative training of intractable discrete graphical models. Our approach builds on a convex quadratic programming relaxation of the MAP inference problem. The model parameters are trained directly within this restricted class of energy functions so as to optimize the predictions on the training data. We address the issue of how to parameterize the resulting model and point out its relation to existing approaches. The primary motivation behind our use of the QP relaxation is its computational efficiency; yet, empirically, its predictive accuracy compares favorably to more expensive approaches. This makes it an appealing choice for many practical tasks. Copyright 2013 by the author(s).},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@conference {Jancsary2012a,
	title = {Loss-specific training of non-parametric image restoration models: A new state of the art},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7578 LNCS},
	number = {PART 7},
	year = {2012},
	pages = {112{\textendash}125},
	abstract = {After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642337857},
	issn = {03029743},
	doi = {10.1007/978-3-642-33786-4_9},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
