-
Notifications
You must be signed in to change notification settings - Fork 39
/
Copy pathresearch.html
1563 lines (1372 loc) · 53.6 KB
/
research.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<html>
<head>
<title>Research</title>
</head>
<body background="images/crysback.jpg" bgcolor="white" lang="EN-US" link="blue" vlink="blue">
<center>
<table border="0">
<tbody><tr>
<th align="center">
<p> <font face="Book Antiqua" size=3 color="brown">
<b> Search, Search, and Research. <br>
On this exploration Odyssey, silence is golden.<br>
Learning comes from errors. <br></b></font>
</p>
</th>
<td align="center">
<image src="images/mountain.jpg" height="100">
</td>
</tr>
</tbody>
</table>
<img src="images/strings.gif" width="550"> <br>
</center>
<p><font face="Arial, Helvetica, sans-serif"><b><font color="#660099", size=3>Research interests</font></b></font>
<blockquote>
My most recent interests are focusing on mathematics for data sciences, in particular topological and geometric methods for high dimensional data analysis and statistical machine learning, with applications in computational biology and information technology.
</blockquote>
<hr>
<p><font face="Arial, Helvetica, sans-serif"><b><font color="#660099", size=3>
Monograph</font></b></font>
<blockquote>
<a href="https://github.com/yao-lab/publication/blob/master/Thesis_YaoY_Berkeley.pdf">A Dynamic Theory of Learning</a>.
PhD Dissertation, University of Calfornia at Berkeley. Supervisor: Steve Smale. December, 2006.
<br>
Published as <i> A Dynamic Theory of Learning -- Online Learning and Stochastic Algorithms in Reproducing Kernel Hilbert Spaces</i>, <a href="http://www.vdm-publishing.com/">Verlag Dr. Muller</a>, ISBN: 978-3-639-09390-2. 2008.
</font>
<br>
</blockquote>
<blockquote>
<a href="https://yao-lab.github.io/book_datasci/">A Mathematical Introduction to Data Analysis</a>.
preprint.
</font>
<br>
</blockquote>
<hr>
<p><font face="Arial, Helvetica, sans-serif"><b><font color="#660099">
Papers and Preprints</font></b></font>
<UL>
<font size=2>
<li>
<B>
Unveiling the hidden reaction kinetic network of carbon dioxide in supercritical aqueous solutions.
</B>
<br>
Chu Li, Yuan Yao, Ding Pan
<br>
<I> Proceedings of the National Academy of Sciences of the United States of America (PNAS)</I>, preprint, 2025.
<br>
[<a href="https://arxiv.org/abs/2401.07019"> arXiv:2401.07019 </a>]
<p>
</li>
<li>
<B>
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery.
</B>
<br>
He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, Yu Li.
<br>
<I>The 31st International Conference on Computational Linguistics (COLING 2025),</I> Abu Dhabi, UAE, January 19-24, 2025.
<br>
[<a href="https://arxiv.org/abs/2311.16208"> arXiv:2311.16208 </a>]
<p>
</li>
<li>
<B>
Deep learning based local feature classification to automatically identify single molecule fluorescence events.
</B>
<br>
Shuqi Zhou, Yu Miao, Haoren Qiu, Yuan Yao, Wenjuan Wang, and Chunlai Chen.
<br>
<I> Communications Biology</I> (Commun Biol), volume 7, Article number: 1404, 2024.
<br>
[<a href="https://www.nature.com/articles/s42003-024-07122-4"> https://doi.org/10.1038/s42003-024-07122-4 </a>]
<p>
</li>
<li>
<B>
UniGAD: Unifying Multi-level Graph Anomaly Detection.
</B>
<br>
Yiqing Lin, Jianheng Tang, Chenyi Zi, H. Vicky Zhao, Yuan Yao, Jia Li.
<br>
<I>The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024),</I> Vancouver, Canada, December 10-15, 2024.
<p>
</li>
<li>
<B>
Towards Global Optimal Visual In-Context Learning Prompt Selection.
</B>
<br>
Chengming Xu, Chen Liu, Yikai Wang, Yuan Yao, Yanwei Fu.
<br>
<I>The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024),</I> Vancouver, Canada, December 10-15, 2024.
<p>
</li>
<li>
<B>
A Biosafety-aware Framework for Generative Enzyme Design with Foundation Models.
</B>
<br>
Xiaoyi Fu, Tao Han, Yuan Yao, Song Guo.
<br>
<I>The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024),</I> Workshop on Foundation Models for Science: Progress, Opportunities, and Challenges (FM4Science), Vancouver, Canada, December 10-15, 2024.
<p>
</li>
<li>
<B>
Mitigating the Alignment Tax of RLHF.
</B>
<br>
Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang.
<br>
<I>The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024),</I> Miami, Florida, November 12-16, 2024.
<br>
[<a href="https://arxiv.org/abs/2309.06256"> arXiv:2309.06256 </a>]
<p>
</li>
<li>
<B>
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes.
</B>
<br>
He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu, Xiangru Tang, Yuan Yao, Yu Li.
<br>
<I>The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024),</I> Miami, Florida, November 12-16, 2024.
<br>
[<a href="https://arxiv.org/abs/2406.13193"> arXiv:2406.13193 </a>]
<p>
</li>
<li>
<B>
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension.
</B>
<br>
Xingyu Lu, He Cao, Zijing Liu, Shengyuan Bai, Leqing Chen, Yuan Yao, Hai-Tao Zheng, Yu Li.
<br>
<I>The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024),</I> Miami, Florida, November 12-16, 2024.
<br>
[<a href="https://arxiv.org/abs/2403.08192"> arXiv:2403.08192 </a>]
<p>
</li>
<li>
<B>
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models.
</B>
<br>
Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song
<br>
<I>The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024),</I> Bangkok, Thailand, August 11-16, 2024.
<br>
[<a href="https://arxiv.org/abs/2311.04044"> arXiv:2311.04044 </a>]
<p>
</li>
<li>
<B>
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion.
</B>
<br>
Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi, Yuan Yao.
<br>
<I>The 41st International Conference on Machine Learning (ICML 2024),</I> Vienna, Austria, July 21-27, 2024.
<br>
[<a href="https://arxiv.org/abs/2402.16305"> arXiv:2402.16305 </a>]
<p>
</li>
<li>
<B>
Learning Long Timescale in Molecular Dynamics by Nano-GPT.
</B>
<br>
Wenqi Zeng, Yuan Yao.
<br>
<I>The 41st International Conference on Machine Learning (ICML 2024),</I> AI for Science Workshop, Vienna, Austria, PMLR 235, July 21-27, 2024.
<br>
[<a href="https://openreview.net/pdf?id=KJEp9C2p6B"> link </a>]
<p>
</li>
<li>
<B>
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation.
</B>
<br>
Hanlin Gu, Jiahuan Luo, Yan Kang, Yuan Yao, Gongxi Zhu, Bowen Li, Lixin Fan, Qiang Yang.
<br>
[<a href="https://arxiv.org/abs/2406.01085"> arXiv:2406.01085 </a>]
<p>
</li>
<li>
<B>
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning.
</B>
<br>
Hangyu Lin, Chen Liu, Chengming Xu, Zhengqi Gao, Yanwei Fu, Yuan Yao.
<br>
<I> The Twelfth International Conference on Learning Representations (ICLR 2024)</I>, Workshop on Bridging the Gap Between Practice and Theory in Deep Learning (BGPT), Vienna, Austria, May 7-11, 2024.
<br>
[<a href="https://arxiv.org/abs/2405.03355"> arXiv:2405.03355 </a>]
<p>
</li>
<li>
<B>
Investigation of the Flipping Dynamics of 1, N6-Ethenoadenine in Alkyladenine DNA Glycosylase.
</B>
<br>
Bin Liu, Yanping Qi, Xiaowei Wang, Xin Gao, Yuan Yao, and Lu Zhang.
<br>
<I>J. Phys. Chem. B</I>, 2024, 128, 7, 1606--1617.
<br>
[<a href="https://doi.org/10.1021/acs.jpcb.3c06853"> DOI: https://doi.org/10.1021/acs.jpcb.3c06853 </a>]
<p>
</li>
<li>
<B>
Split Knockoffs for Multiple Comparisons: Controlling the Directional False Discovery Rate.
</B>
<br>
Yang Cao, Xinwei Sun, Yuan Yao
<br>
<I>Journal of the American Statistical Association</I>, Volume 119, Issue 548, Pages 2822-2832, 2024. DOI: 10.1080/01621459.2023.2279292
<br>
[<a href="https://arxiv.org/abs/2310.07605"> arXiv:2310.07605 </a>][<a href="https://doi.org/10.1080/01621459.2023.2279292"> https://doi.org/10.1080/01621459.2023.2279292 </a>]
<p>
</li>
<li>
<B>
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning.
</B>
<br>
Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang
<br>
[<a href="https://arxiv.org/abs/2309.02476"> arXiv:2309.02476 </a>]
<p>
</li>
<li>
<B>
Random Smoothing Regularization in Kernel Gradient Descent Learning.
</B>
<br>
Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan Yao
<br>
<I>Journal of Machine Learning Research</I>, 25(284): 1-88, 2024.
<br>
[<a href="https://arxiv.org/abs/2305.03531"> arXiv:2305.03531 </a>]
<p>
</li>
<li>
<B>
Inducing Neural Collapse in Deep Long-tailed Learning.
</B>
<br>
Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Lujia Pan, Yuan Yao
<br>
<I>Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)</I>, Valencia, Spain, PMLR: Volume 206. April 25-27, 2023.
<br>
[<a href="https://arxiv.org/abs/2302.12453"> arXiv:2302.12453 </a>]
<p>
</li>
<li>
<B>
SurfBind: Surface Distance aided Geometric Deep Learning for Binding Conformations.
</B>
<br>
Jiamin Wu, Wenqi Zeng, Song Liu, Yuan Yao
<br>
<I>The 37th AAAI Conference on Artificial Intelligence: The Ninth International Workshop on Deep Learning on Graphs (DLG-AAAI'23)</I>, Washington, DC, USA. February 7-14, 2023.
<br>
[<a href="publications/DLG-AAAI23_paper_9034.pdf"> pdf </a>] [<a href="https://drive.google.com/file/d/1Kopa5OLQ3XEL7cpT4xKwYS14zgfiLjMo/view"> link </a>]
<p>
</li>
<li>
<B>
Exploring Vision Transformers as Diffusion Learners.
</B>
<br>
He Cao, J Wang, T Ren, X Qi, Y Chen, Y Yao, L Zhang
<br>
[<a href="https://arxiv.org/abs/2212.13771"> arXiv:2212.13771 </a>]
<p>
</li>
<li>
<B>
StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding.
</B>
<br>
Jinshan Zeng, Y Wang, Q Chen, Y Liu, M Wang, Y Yao
<br>
[<a href="https://arxiv.org/abs/2211.06198"> arXiv:2211.06198 </a>]
<p>
</li>
<li>
<B>
Optimal Private Streaming SCO in $\ell_p$-geometry with Applications in High Dimensional Online Decision Making.
</B>
<br>
Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, and Jiheng Zhang
<br>
<I>Proceedings of the 39th International Conference on Machine Learning (ICML)</I>, Baltimore, Maryland, USA, PMLR 162, July 17-23, 2022.
<br>
[<a href="https://arxiv.org/abs/2206.08111"> arXiv:2206.08111 </a>][<a href="https://proceedings.mlr.press/v162/han22d/han22d.pdf"> icml version </a>][<a href="https://github.com/liangzp/DP-Streaming-SCO"> GitHub </a>]
<p>
</li>
<li>
<B>
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release.
</B>
<br>
Donghao Li, Yang Cao, Yuan Yao
<br>
<I> Conference on Parsimony and Learning (CPAL)</I>, Hong Kong, PMLR 234:480-514, Jan 3-6, 2024.
<br>
[<a href="https://arxiv.org/abs/2202.06467"> arXiv:2202.06467 </a>][<a href="https://proceedings.mlr.press/v234/li24b/li24b.pdf"> link </a>][<a href="https://openreview.net/forum?id=HyOziZFh5x"> openreview </a>]
<p>
</li>
<li>
<B>
SARS-CoV-2 RNA-Dependent RNA Polymerase Follows Asynchronous Translocation Pathway for Viral Transcription and Replication.
</B>
<br>
Xiaowei Wang, Tiantian Xu, Yuan Yao, Peter Pak-Hang Cheung, Xin Gao, and Lu Zhang
<br>
<I>The Journal of Physical Chemistry Letters</I>, 14(45):10119–10128, Nov. 3, 2023.
<br>
[<a href="https://chemrxiv.org/engage/chemrxiv/article-details/62a95803eb1f0c07e9896091"> DOI: 10.26434/chemrxiv-2022-7t1jg </a>][<a href="https://doi.org/10.1021/acs.jpclett.3c01249"> https://doi.org/10.1021/acs.jpclett.3c01249 </a>]
<p>
</li>
<li>
<B>
Unveiling the ``Template-dependent" Inhibition on the Viral Transcription of SARS-CoV-2.
</B>
<br>
Luo, Xueying, Xiaowei Wang, Yuan Yao, Xin Gao, and Lu Zhang
<br>
<I>The Journal of Physical Chemistry Letters</I>. 13(31): 7197-7205, July 30, 2022.
<br>
[<a href="https://doi.org/10.1021/acs.jpclett.2c01314"> DOI: 10.1021/acs.jpclett.2c01314 </a>]
<p>
</li>
<li>
<B>
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces.
</B>
<br>
<a href="https://yanweifu.github.io/">Yanwei Fu</a>, Chen Liu, Donghao Li, Zuyuan Zhong, Xinwei Sun, Jinshan Zeng, and Yuan Yao.
<br>
<I>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</I>, 45(2):1749-1765, 2023.
<br>
[<a href="https://arxiv.org/abs/1905.09449"> arXiv:1905.09449 </a>][<a href="https://ieeexplore.ieee.org/document/9762064"> DOI: 10.1109/TPAMI.2022.3168881</a>][<a href="https://github.com/DessiLBI2020/DessiLBI"> GitHub source </a>]
<p>
</li>
<li>
<B>
RPnet: A Reverse Projection Based Neural Network for Coarse-graining Metastable Conformational States for Protein Dynamics.
</B>
<br>
Hanlin Gu, Wei Wang, Siqin Cao, Ilona Christy Unarta, Yuan Yao, Fu Kit Sheong, Xuhui Huang
<br>
<I>Physical Chemistry Chemical Physics</I>, 2022, 24, 1462-1474. DOI: 10.1039/D1CP03622J
<br>
[<a href="https://doi.org/10.1101/2021.08.04.455071"> https://www.biorxiv.org/content/10.1101/2021.08.04.455071v1 </a>]
<p>
</li>
<li>
<B>
Federated Deep Learning with Bayesian Privacy.
</B>
<br>
Hanlin Gu, Lixin Fan, Bowen Li, Yan Kang, Yuan Yao, Qiang Yang
<br>
[<a href="https://arxiv.org/abs/2109.13012"> arXiv:2109.13012 </a>]
<p>
</li>
<li>
<B>
A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer.
</B>
<br>
Wenqi Zeng, Siqin Cao, Xuhui Huang, Yuan Yao
<br>
[<a href="https://arxiv.org/abs/2107.06573"> arXiv:2107.06573 </a>][<a href="https://github.com/Wendysigh/LSTM-Transformer-for-MD"> Github </a>]
<p>
</li>
<li>
<B>
An Efficient Method to Quantify Structural Distributions in Heterogeneous cryo-EM Datasets.
</B>
<br>
Hanlin Gu, Wei Wang, Ilona Christy Unarta, Wenqi Zeng, Fu Kit Sheong, Peter Cheung, Song Liu, Yuan Yao, and Xuhui Huang
<br>
[<a href="https://doi.org/10.1101/2021.05.27.446075"> bioRxiv:10.1101/2021.05.27.446075 </a>][<a href="https://github.com/ghl1995/LCTC"> Github </a>]
<p>
</li>
<li>
<B>
Controlling the False Discovery Rate in Transformational Sparsity: Split Knockoffs.
</B>
<br>
Yang Cao, Xinwei Sun and Yuan Yao
<br>
<I>Journal of Royal Statistical Society: Series B (Statistical Methodology)</I>, Volume 86, Issue 2, April 2024, Pages 386-410. DOI:10.1093/jrsssb/qkad126
<br>
[<a href="https://arxiv.org/abs/2103.16159"> arXiv:2103.16159 </a>][<a href="https://doi.org/10.1093/jrsssb/qkad126">DOI:10.1093/jrsssb/qkad126 </a>][<a href="https://github.com/yao-lab/split_knockoffs"> Github for Matlab </a>][<a href="https://cran.r-project.org/web/packages/SplitKnockoff/"> R package, by Haoxue WANG </a>]
<p>
</li>
<li>
<B>
Evaluating Visual Properties via Robust HodgeRank
</B>
<br>
Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang and Yuan Yao
<br>
<I>International Journal of Computer Vision</I>, 129: 1732-1753, 2021.
<br>
[<a href="http://arxiv.org/abs/1408.3467"> arXiv:1408.3467 </a>] [<a href="https://doi.org/10.1007/s11263-021-01438-y"> DOI: 10.1007/s11263-021-01438-y </a>]
<p>
</li>
<li>
<B>
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization.
</B>
<br>
Jinshan Zeng, Yixuan Zha, Ke Ma, Yuan Yao
<br>
[<a href="https://arxiv.org/abs/2101.00236"> arXiv:2101.00236 </a>]
<p>
</li>
<li>
<B>
Not All Samples are Trustworthy: Towards Deep Robust SVP Prediction.
</B>
<br>
Xu, Qianqian; Yang, Zhiyong; Jiang, Yangbangyan; Cao, Xiaochun; Yao, Yuan; Huang, Qingming
<br>
<I>IEEE Transactions on Pattern Analysis and Machine Intelligence</I>, 44(6):3154-3169, June 2022.
<br>
[DOI:<a href="https://doi.org/10.1109/TPAMI.2020.3047817"> 10.1109/TPAMI.2020.3047817 </a>][<a href="https://github.com/qianqianxu010/TPAMI21-Not-All-Samples-are-Trustworthy"> GitHub </a>]
<p>
</li>
<li>
<B>
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding.
</B>
<br>
Jinshan Zeng, Qi Chen, Yunxin Liu, Mingwen Wang, Yuan Yao
<br>
<I> AAAI Conference on Artificial Intelligence (AAAI)</I>, Feb 2-9, 2021.
<br>
[<a href="https://arxiv.org/abs/2012.08687"> arXiv:2012.08687 </a>][<a href="https://github.com/JinshanZeng/StrokeGAN"> GitHub </a>]
<p>
</li>
<li>
<B>
Deep Partial Rank Aggregation for Personalized Attributes.
</B>
<br>
Qianqian Xu, Zhiyong Yang, Zuyao Chen, Yangbangyan Jiang, Xiaochun Cao, Yuan Yao and Qingming Huang.
<br>
<I> AAAI Conference on Artificial Intelligence (AAAI)</I>, Feb 2-9, 2021.
<br>
[<a href="https://github.com/qianqianxu010/AAAI21/blob/main/AAAI21-DeepPartialRank.pdf"> pdf </a>][<a href="https://github.com/qianqianxu010/AAAI21"> GitHub </a>]
<p>
</li>
<li>
<B>
Adversarial Robustness of Stabilized Neural ODEs Might be from Obfuscated Gradients.
</B>
<br>
Yifei Huang, <a href="https://yaodongyu.github.io/">Yaodong Yu</a>, <a href="https://hongyanz.github.io/">Hongyang Zhang</a>, <a href="https://people.eecs.berkeley.edu/~yima/">Yi Ma</a>, Yuan Yao
<br>
<I> Mathematical and Scientific Machine Learning (MSML)</I>, August 16-19, 2021.
<br>
[<a href="https://arxiv.org/abs/2009.13145"> arXiv:2009.13145 </a>][<a href="https://github.com/yao-lab/SONet"> GitHub </a>]
<p>
</li>
<li>
<B>
Generative Adversarial Networks for Robust Cryo-EM Image Denoising.
</B>
<br>
Hanlin Gu, Ilona Christy Unarta, Xuhui Huang, Yuan Yao
<br>
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, by Ke Chen (Editor), Carola-Bibiane Sch\"{o}nlieb (Editor), Xue-Cheng Tai (Editor), Laurent Younes (Editor), Springer, 2022.
<br>
[<a href="https://arxiv.org/abs/2008.07307"> arXiv:2008.07307 </a>][<a href="https://doi.org/10.1007/978-3-030-03009-4_126-1"> https://doi.org/10.1007/978-3-030-03009-4_126-1 </a>]
<p>
</li>
<li>
<B>
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning.
</B>
<br>
Yikai Wang, Li Zhang, Yuan Yao, <a href="https://yanweifu.github.io/">Yanwei Fu</a>
<br>
<I>IEEE Transactions on Pattern Analysis and Machine Intelligence</I>, 44(10):6240-6253, 01 October 2022.
<br>
[<a href="https://arxiv.org/abs/2007.08461"> arXiv:2007.08461 </a>][DOI:<a href="https://doi.org/10.1109/TPAMI.2021.3086140"> 10.1109/TPAMI.2021.3086140 </a>]
<p>
</li>
<li>
<B>
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths.
</B>
<br>
<a href="https://yanweifu.github.io/">Yanwei Fu</a>, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao
<br>
<I>Thirty-seventh International Conference on Machine Learning (ICML)</I>, July 12-18, 2020.
<br>
[<a href="https://arxiv.org/abs/2007.02010"> arXiv:2007.02010 </a>][<a href="https://arxiv.org/abs/1905.09449"> early report as arXiv:1905.09449 </a>][<a href="https://proceedings.icml.cc/static/paper_files/icml/2020/1977-Paper.pdf"> link </a>][<a href="https://github.com/DessiLBI2020/DessiLBI"> GitHub </a>]
<p>
</li>
<li>
<B>
Differentially Private Federated Learning with Laplacian Smoothing.
</B>
<br>
Zhicong Liang, <a href="https://www.math.ucla.edu/~wangbao/">Bao Wang</a>, <a href="http://web.cs.ucla.edu/~qgu/">Quanquan Gu</a>, <a href="https://www.math.ucla.edu/~sjo/">Stanley Osher</a>, Yuan Yao.
<br>
<I>Applied and Computational Harmonic Analysis</I>, 2024, 72:101660. Short report appeared in <I>NeurIPS Workshop on Federated Learning for Data Privacy and Confidentiality</I>, Vancouver, Canada, Dec. 8-14, 2019.
<br>
[<a href="https://arxiv.org/abs/2005.00218"> arXiv:2005.00218 </a>][<a href="https://github.com/zliangak/dp-fed-ls"> GitHub </a>][<a href="https://doi.org/10.1016/j.acha.2024.101660"> DOI: https://doi.org/10.1016/j.acha.2024.101660 </a>]
<p>
</li>
<li>
<B>
Learning the mapping $x\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack.
</B>
<br>
<a href="https://math.berkeley.edu/people/grad/jiefu-zhang">Jiefu Zhang</a>, Leonardo Zepeda-Núñez, Yuan Yao, <a href="https://math.berkeley.edu/~linlin/">Lin Lin</a>.
<br>
<I>Communications on Applied Mathematics and Computation</I>, 3, 313-335 (2021).
<br>
[<a href="https://arxiv.org/abs/2002.10561"> arXiv:2002.10561 </a>][<a href="https://link.springer.com/article/10.1007/s42967-020-00078-2"> https://doi.org/10.1007/s42967-020-00078-2 </a>]
<p>
</li>
<li>
<B>
Who Likes What? – SplitLBI in Exploring Preferential Diversity of Ratings.
</B>
<br>
<a href="https://qianqianxu010.github.io/">Qianqian Xu</a>, Jiechao Xiong, Zhiyong Yang, Xiaochun Cao, Qingming Huang and Yuan Yao.
<br>
<I> AAAI Conference on Artificial Intelligence (AAAI)</I>, New York, Feb 7-12, 2020.
<br>
[ DOI:<a href="https://doi.org/10.1609/aaai.v34i01.5359"> https://doi.org/10.1609/aaai.v34i01.5359 </a>][<a href="https://github.com/qianqianxu010/AAAI2020"> code </a>]
<p>
</li>
<li>
<B>
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics.
</B>
<br>
Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan Yao, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou.
<br>
<I> AAAI Conference on Artificial Intelligence (AAAI)</I>, New York, Feb 7-12, 2020.
<br>
[<a href="https://arxiv.org/abs/1910.02249"> arXiv:1910.02249 </a>]
<p>
</li>
<li>
<B>
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI.
</B>
<br>
<a href="https://qianqianxu010.github.io/">Qianqian Xu</a>, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang and Yuan Yao.
<br>
<I> Annual Conference on Neural Information Processing Systems (NeurIPS)</I>, Vancuver, Canada, 3896–3906, 2019.
<br>
[<a href="https://github.com/yao-lab/NeurIPS2019-iSplitLBI/blob/master/paper/NeurIPS19-iSplitLBI.pdf"> pdf </a>][<a href="https://github.com/yao-lab/NeurIPS2019-iSplitLBI"> code </a>][<a href="https://github.com/yao-lab/NeurIPS2019-iSplitLBI/blob/master/poster/NeurIPS2019-iSplitLBI-Poster.pdf"> poster </a>]
<p>
</li>
<li>
<B>
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size.
</B>
<br>
Ke Ma, Jinshan Zeng, Jiechao Xiong, <a href="https://qianqianxu010.github.io/">Qianqian Xu</a>, Xiaochun Cao, Wei Liu and Yuan Yao.
<br>
<I>IEEE Transactions on Knowledge and Data Engineering (TKDE)</I>, June 2021, 33(6): 2467-2478. Extended from AAAI'18 version.
<br>
[<a href="https://ieeexplore.ieee.org/document/8918070"> 10.1109/TKDE.2019.2956700 </a>]
<p>
</li>
<li>
<B>
$S^2$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning.
</B>
<br>
<a href="https://yanweifu.github.io/">Yanwei Fu</a>, Donghao Li, Xinwei Sun, Shun Zhang, Yizhou Wang, and Yuan Yao.
<br>
[<a href="https://arxiv.org/abs/1904.10873"> arXiv:1904.10873 </a>][<a href="https://github.com/corwinliu9669/dS2LBI"> GitHub </a>]
<p>
</li>
<li>
<B>
Deep Robust Subjective Visual Property Prediction in Crowdsourcing.
</B>
<br>
<a href="https://qianqianxu010.github.io/">Qianqian Xu</a>, Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang, Yuan Yao.
<br>
<I> Conference on Computer Vision and Pattern Recognition (CVPR)</I>, Long Beach, CA, June 16-20, 2019
<br>
[<a href="https://arxiv.org/abs/1903.03956"> arXiv:1903.03956 </a>][<a href="https://github.com/qianqianxu010/CVPR19-Deep-Robust-Subjective-Visual-Property-Prediction-in-Crowdsourcing"> GitHub source </a>]
<p>
</li>
<li>
<B>
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective.
</B>
<br>
<a href="https://www.stat.uchicago.edu/~chaogao/">Chao Gao</a>, Yuan Yao, Weizhi Zhu.
<br>
<I>Journal of Machine Learning Research</I>, 21(160):1-48, 2020.
<br>
[<a href="https://arxiv.org/abs/1903.01944"> arXiv:1903.01944 </a>][<a href="https://www.jmlr.org/papers/v21/19-462.html"> link </a>][<a href="https://github.com/zhuwzh/Robust-GAN-Scatter"> Github Source </a>]
<p>
</li>
<li>
<B>
On Global Linear Convergence in Stochastic Nonconvex Optimization for Semidefinite Programming.
</B>
<br>
Jinshan Zeng, Ke Ma, Yuan Yao.
<br>
<I>IEEE Transactions on Signal Processing</I>, Vol. 67, No. 16, Page: 4261 - 4275, August 15, 2019.
<br>
[ DOI:<a href="https://ieeexplore.ieee.org/document/8752037"> 10.1109/TSP.2019.2925609 </a>][ <a href=""> <I> 2020 ICCM Distinguished Paper Award </I></a> ]
<p>
</li>
<li>
<B>
On ADMM in Deep Learning: Convergence and Saturation-Avoidance.
</B>
<br>
Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou.
<br>
<I>Journal of Machine Learning Research</I>, 22(199): 1-67, 2021.
<br>
[<a href="https://arxiv.org/abs/1902.02060"> arXiv:1902.02060 </a>][<a href="https://jmlr.org/papers/v22/20-1006.html"> link </a>][<a href="https://github.com/JinshanZeng/ADMM-DeepLearning"> GitHub 1</a>][<a href="https://github.com/yao-lab/ADMM-DeepLearning"> GitHub 2</a>]
<p>
</li>
<li>
<B>
Zero-Shot Learning via Recurrent Knowledge Transfer.
</B>
<br>
Bo Zhao, Xinwei Sun, Xiaopeng Hong, Yuan Yao, and Yizhou Wang.
<br>
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 7-11 Jan. 2019.
<br>
[<a href="https://doi.org/10.1109/WACV.2019.00144"> DOI: 10.1109/WACV.2019.00144 </a>] [<a href="https://github.com/yao-lab/Zero-shot-Learning-via-Recurrent-Knowledge-Transfer"> GitHub source </a>]
<p>
</li>
<li>
<B>
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin Dynamics.
</B>
<br>
Weizhi Zhu, Yifei Huang, Yuan Yao.
<br>
<I>Frontiers in Applied Mathematics and Statistics</I>, special section on Mathematics of Computation and Data Science, 30 October 2020. This work was presented at ICCM'2019 in Beijing, which appeared in <I> Annals of Mathematical Sciences and Applications</I>,
Volume 7, Number 2, pages: 221-258, 2022.
<br>
[<a href="https://arxiv.org/abs/1810.03389"> arXiv:1810.03389 </a>][<a href="https://doi.org/10.3389/fams.2020.575073"> DOI:10.3389/fams.2020.575073 </a>][<a href="https://dx.doi.org/10.4310/AMSA.2022.v7.n2.a4"> DOI: https://dx.doi.org/10.4310/AMSA.2022.v7.n2.a4 </a> ]
<p>
</li>
<li>
<B>
Robust Estimation and Generative Adversarial Networks.
</B>
<br>
<a href="https://www.stat.uchicago.edu/~chaogao/">Chao Gao</a>, Jiyi Liu, Yuan Yao, Weizhi Zhu.
<br>
<I>International Conference on Learning Representations (ICLR)</I>, New Orleans, Louisiana, United States, May 6 - May 9, 2019.
<br>
[<a href="https://arxiv.org/abs/1810.02030"> arXiv:1810.02030 </a>][<a href="https://openreview.net/forum?id=BJgRDjR9tQ"> ICLR'19 </a>][<a href="https://github.com/zhuwzh/Robust-GAN-Center"> GitHub source </a>]
<p>
</li>
<li>
<B>
Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification.
</B>
<br>
Yin Xian, Hanlin Gu, Wei Wang, Xuhui Huang, Yuan Yao, Yang Wang, Jian-Feng Cai.
<br>
<I>The 6th IEEE Global Conference on Signal and Information Processing</I>, Anaheim, California, Nov 26-29, 2018.
<br>
[<a href="https://arxiv.org/abs/1810.08829"> arXiv:1810.08829 </a>] [<a href="https://github.com/poline3939/Cryo-EM-Denoising"> GitHub source </a>]
<p>
</li>
<li>
<B>
A Margin-based MLE for Crowdsourced Partial Ranking.
</B>
<br>
<a href="https://qianqianxu010.github.io/">Qianqian Xu</a>, Jiechao Xiong, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao.
<br>
<I>In 2018 ACM Multimedia Conference (MM’18), October 22–26, 2018, Seoul, Republic of Korea. ACM, New York, NY, USA.</I>
<br>
[<a href="https://arxiv.org/abs/1807.11014"> arXiv:1807.11014 </a>]
<p>
</li>
<li>
<B>
FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis
</B>
<br>
Xinwei Sun, Lingjing Hu, Fandong Zhang, Yuan Yao, Yizhou Wang
<br>
<I>The 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain, 2018</I>
<br>
[<a href="https://arxiv.org/abs/1807.08125"> arXiv:1807.08125 </a>]
<p>
</li>
<li>
<B>
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
</B>
<br>
Bo Zhao, Xinwei Sun, <a href="https://yanweifu.github.io/">Yanwei Fu</a>, Yuan Yao, Yizhou Wang
<br>
<I>The 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018</I>
<br>
[<a href="https://arxiv.org/abs/1806.04360"> arXiv:1806.04360 </a>] [<a href="http://proceedings.mlr.press/v80/zhao18c.html"> link </a>][<a href="https://github.com/yao-lab/MSplitLBI"> GitHub source1 </a>][<a href="https://github.com/PatrickZH/MSplitLBI"> GitHub source2</a>]
<p>
<li>
<B>
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
</B>
<br>
Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang, Yuan Yao
<br>
<I>IEEE Transactions on Pattern Analysis and Machine Intelligence</I>, 41(4):844-856, 2019. Extended from MM'16 in [<a href="https://arxiv.org/abs/1607.03401"> arXiv:1607.03401 </a>].
<br>
[<a href="https://arxiv.org/abs/1804.11177"> arXiv:1804.11177 </a>] [<a href="https://ieeexplore.ieee.org/document/8319957/"> doi: 10.1109/TPAMI.2018.2817205 </a>][<a href="https://github.com/yao-lab/TPAMI2018"> GitHub source</a>]
<p>
<li>
<B>
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
</B>
<br>
Tim Tsz-Kit Lau, Jinshan Zeng, Baoyuan Wu, Yuan Yao
<br>
<I>The 6th International Conference on Learning Representations (ICLR 2018), Workshop Track</I>
<br>
[<a href="https://arxiv.org/abs/1803.09082"> arXiv:1803.09082 </a>]
<p>
<li>
<B>
Global Convergence of Block Coordinate Descent in Deep Learning
</B>
<br>
Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao
<br>
<I>The 36th International Conference on Machine Learning (ICML)</I>, Long Beach, California, June 10-15, 2019.
<br>
[<a href="https://arxiv.org/abs/1803.00225"> arXiv:1803.00225 </a>]
<p>
</li>
<li>
<B>
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
</B>
<br>
Jinshan Zeng, Ke Ma, Yuan Yao
<br>
<I>Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)</I>, Lanzarote, Spain, 2018.
<br>
[<a href="https://arxiv.org/abs/1802.06232"> arXiv:1802.06232 </a>] [<a href="http://proceedings.mlr.press/v84/zeng18a/zeng18a.pdf"> link </a>]
<p>
</li>
<li>
<B>
A Unified Dynamic Approach to Sparse Model Selection.
</B>
<br>
Chendi Huang, Yuan Yao.
<br>
<I>Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)</I>, Lanzarote, Spain, 2018.
<br>
[<a href="https://arxiv.org/abs/1810.03608"> arXiv:1810.03608 </a>] [<a href="http://proceedings.mlr.press/v84/huang18a/huang18a.pdf"> link </a>]
<p>
</li>
<li>
<B>
Stochastic Non-Convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
</B>
<br>
Ke Ma, Jinshan ZENG, Jiechao Xiong, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan Yao
<br>
<I> The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)</I>, New Orleans, Louisiana, Feb 2-7, 2018.
<br>
[<a href="https://arxiv.org/abs/1711.06446"> arXiv:1711.06446 </a>] [<a href="https://github.com/alphaprime/Stabilized_Stochastic_BB"> GitHub Source in Matlab </a>]
<p>
</li>
<li>
<B>
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
</B>
<br>
Qianqian Xu, Jiechao Xiong, Xi Chen, Qingming Huang, Yuan Yao
<br>
<I> The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)</I>, New Orleans, Louisiana, Feb 2-7, 2018.
<br>
[<a href="https://arxiv.org/abs/1711.05957"> arXiv:1711.05957 </a>] [<a href="https://github.com/yuany-pku/activesample"> GitHub </a>]
<p>
</li>
<li>
<B>
Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics
</B>
<br>
Tsz Kit Lau and Yuan Yao
<br>
<I>The 10th NIPS Workshop on Optimization for Machine Learning (NIPS 2017)</I>, Long Beach, California, Dec 3-8, 2017.
<br>
[<a href="https://arxiv.org/abs/1710.05338"> arXiv:1710.05338 </a>]
<p>
</li>
<li>
<B>
Exploring Outliers in Crowdsourced Ranking for QoE
</B>
<br>
Qianqian Xu, Ming Yan, Chendi Huang, Jiechao Xiong, Qingming Huang, and Yuan Yao
<br>
<I> ACM Multimedia 2017 (Oral presentation)</I>, Mountain View, California, Oct 23-27, 2017.
<br>
[<a href="https://arxiv.org/abs/1707.07539"> arXiv:1707.07539 </a>]
<p>
</li>
<li>
<B>
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
</B>
<br>
Xinwei Sun, Lingjing Hu, Yuan Yao, and Yizhou Wang
<br>
<I> Medical Image Computing and Computer Assisted Interventions Conference (MICCAI)</I>, Quebec City, Canada, Sept 10-14, 2017.
<br>
[<a href="https://arxiv.org/abs/1705.09249"> arXiv:1705.09249 </a>]
<p>
</li>
<li>
<B>
Boosting with Structural Sparsity: A Differential Inclusion Approach
</B>
<br>
Chendi Huang, Xinwei Sun, Jiechao Xiong, and Yuan Yao
<br>
<I>Applied and Computational Harmonic Analysis</I>, 48(1): 1-45, 2020.
<br>
[<a href="https://arxiv.org/abs/1704.04833"> arXiv:1704.04833 </a>] [<a href="https://doi.org/10.1016/j.acha.2017.12.004"> https://doi.org/10.1016/j.acha.2017.12.004 </a>]
<p>
</li>
<li>
<B>
A Statistical Learning Approach for Drug Sensitivity Prediction with Cancer Cell Line Data
</B>
<br>
Lijing Wang, Yangzhong Tang, Stevan Djakovic, Julie Rice, Tony Wu, Daniel J. Anderson, and Yuan Yao
<br>
<I><a href="http://dahshu.org/events/cph2017/index.html">Dahshu 2017: Data Science and Computational Precision Health</a></I>, San Francisco, Feb 20-22, 2017.
<br>
[<a href="publications/poster_CleaveBioCPH2017_ForReview.pdf"> Poster </a>][<a href="https://www.kaggle.com/c/drugsensitivity/"> Dataset Released in Kaggle </a>]
<p>
<li>
<B>
Split LBI: An Iterative Regularization Path with Structural Sparsity
</B>
<br>
Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
<br>
<I>Advances in Neural Information Processing Systems 29 (NIPS 2016)</I>, Barcelona, Spain, December 5-10, 2016.
<br>
[<a href="https://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity"> NIPS link </a>] [ <a href="https://github.com/yuany-pku/split-lbi"> Matlab codes at GitHub </a>]
<p>
<li>
<B>
Learning rates of regression with q-norm loss and threshold